Download Factors influencing information needs among cancer patients: A

Library & Information Science Research 28 (2006) 7 – 23
Factors influencing information needs among cancer
patients: A meta-analysis
Kalyani Ankem
Associate Professor, School of Library and Information Sciences, North Carolina Central University,
Durham, NC 27707, USA
Abstract
Receiving the right information is extremely important for cancer patients as they move through the
illness trajectory. However, according to previous research, not all patients are alike in the amount of
information they need. A meta-analysis was conducted to study the aggregate influence of
demographic and situational variables on the amount of information cancer patients need. The intent
was to provide those individuals involved in information exchanges with indicators – patient
characteristics and/or patient situations – to avoid overwhelming patients who need less information as
well as to satisfy the higher needs of patients who need greater amounts of information. The pooled
effect sizes produced show that younger cancer patients need more information (r = 0.26) and
patients who preferred active roles in treatment decision-making express greater need for information
(r = 0.38). Cancer patients’ gender, thier education, time elapsed since diagnosis of cancer, and the
stage of their illness may not be related to their information needs. In particular, it may be important to
probe further the reasons for the lower need for information among elderly cancer patients because if
their lower needs are due to their feelings of inhibition, efforts should be made in all contexts of health
information transfer to enhance their information seeking.
D 2005 Elsevier Inc. All rights reserved.
1. Introduction
A diagnosis of cancer is a fearful experience. Both the diagnosis and the ensuing treatment
require patients to learn about many areas of their illness, making health information
E-mail address: [email protected].
0740-8188/$ - see front matter D 2005 Elsevier Inc. All rights reserved.
doi:10.1016/j.lisr.2005.11.003
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K. Ankem / Library & Information Science Research 28 (2006) 7–23
extremely important. It has been shown that, in addition to facilitating treatment, the
information that patients receive about the various aspects of cancer can be beneficial to them
in their functional adjustment as well as their coping (Cassileth, Volckmar, & Goodman,
1980a; Iconomou, Viha, Koutras, Vagenakis, & Kalofonos, 2002).
Although the benefits of possessing information are known, not all patients are alike in
their need for information. Researchers have found that patients’ demographics, the situations
they find themselves in during illness, and their psychological states can determine the need
for and receptivity to cancer-related information. Demographics which may influence
information need among cancer patients are age, education, and gender. Concurrently, several
aspects of their situation, such as (1) time since diagnosis, (2) type of treatment which patients
are undergoing, (3) severity of disease based on the stage of cancer, and (4) the role patients
prefer to play in making decisions related to treatment may influence need for cancer-related
information. Also, patients’ psychological states – level of anxiety, presence of depression,
and feelings of control – may affect information needs.
These factors – demographics, situations, and psychological states – can alter the need for
specific types of information in a patient, for instance, the need for disease-related
information, treatment-related information, or any other type of cancer-related information.
However, the literature covering the effects on types of information needs is limited, and it is
mostly descriptive. More research with statistical evidence of the impact of demographics and
situations on the overall need for cancer-related information has been reported; however, this
research lacks evidence of the impact of psychological states. In these studies, patients’ needs
for specific types of information are usually combined to create a general indicator of overall
need.
2. Problem statement
Meta-analysis involves the quantitative synthesis of relevant research results across
existing studies and is generally referred to as such to distinguish it from a systematic review
which does not involve actual mathematical combination. Quantitative synthesis probes the
influence of select demographic and situational factors on these patients’ overall need for
information. The following research questions establish the relationships between demographic and situational variables and overall information need to be analyzed in the metaanalytic study.
(1) What are the aggregate relationships between demographic variables – age and
education – and overall information need among cancer patients?
(2) What are the aggregate relationships between situational variables – time since
diagnosis, stage of cancer, and preferred role in treatment decision-making – and overall
information need among cancer patients?
The value of this meta-analysis lies in its ability to show the aggregate effects of
demographics and situations to provide preliminary guidance to professionals in identifying
K. Ankem / Library & Information Science Research 28 (2006) 7–23
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patients who, due to certain personal characteristics or illness situations, or both, are less or
more receptive to cancer-related information. For instance, some patients have difficulty
dealing with the full extent of this information, particularly all the information on adverse
effects. The identification of these patients will allow individuals involved in information
exchanges to be aware of patients who would be overwhelmed by greater amounts of
information during a highly stressful experience in their lives. This report will also indicate
the characteristics and situations of patients who have a higher need for information. Through
the identification of these patient preferences for information need, their needs can be
satisfied and stress minimized.
3. Factors affecting need for cancer-related information
3.1. Demographics and information needs
In the literature, male and female cancer patients were found to be no different in their need
for information. The more educated cancer patients, in earlier research, and the younger
cancer patients, in several research studies through the decades, showed a greater need for
information. The link between education and greater information need was established by
Cassileth and colleagues in their seminal work in 1980, when they reported that more
educated cancer patients wanted more information (Cassileth et al., 1980a; Cassileth, Zupkis,
Sutton-Smith, & March, 1998b). In recent literature, however, this effect of education on
information needs has been weaker. Cassileth and colleagues also reported that younger
patients needed more information than older patients. Since Cassileth and colleagues’ study,
other studies have substantiated this relationship between age and information needs
(Harrison-Woermke & Graydon, 1993), while some have not found younger cancer patients
to be any different in the need for information compared to those patients who are older.
The question still remains as to the true effects of education and age on cancer patients’
receptivity to information. Do the more educated need more information? Also, do younger
cancer patients want more information, whether it is due to different coping styles or a different
acculturation process into the health care system (Turk-Charles, Meyerowitz, & Gatz, 1997)?
Or, are younger patients no different from their older counterparts in information needs?
3.2. Situation and information needs
In addition, the effects of (1) time as it passes after a patient is diagnosed with cancer, (2)
the type of treatment a patient receives during illness, (3) the stage of a patient’s cancer, and
(4) a patient’s behavioral preference for a role in making illness-related decisions have been
analyzed to understand if situations such as these that patients find themselves in during
illness can change their need for information.
The type of treatment a patient receives for cancer was found to have no effect on his or
her need for information, in the literature. More ambiguous was the effect of time passed
since a patient was first diagnosed with cancer on information needs. Some evidence
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existed in the literature to support that patients need less information as time passed during
illness (Harrison-Woermke & Graydon, 1993). Accordingly, information needs may not be
constant over time because patients’ familiarity with disease increased over time leading to
lower need for information (Iconomou et al., 2002). Other researchers have found that
patients need considerable amounts of information throughout their illness because cancer
patients move through different experiences which create new information needs during the
course of their illness (Harrison, Galloway, Graydon, Palmer-Wickham, & Rich-van der
Bij, 1999). Similar contradictions have been evidenced for the effect of the stage of a
patient’s cancer on need for information (Harrison-Woermke & Graydon, 1993; Iconomou
et al., 2002), leaving it unanswered whether patients have more questions due to severity of
illness.
Somewhat different from the situations above is the patient’s preference for a role in
illness-related decisions; that is, the patient’s preferred role is focused on how the patient
responds to the health care environment as opposed to the other situations which are more or
less external to the patient. Some evidence, again, existed in the literature to support that
patients who desired an active or a collaborative role rather than a passive role in making
health care decisions also need more information (Hack, Degner, & Dyck, 1994).
As with the influence of demographics, questions remained about the effects of time and
stage of cancer on cancer patients’ need for information and about the magnitude of the effect
of a patient’s preference for a more involved role in health care decisions on need for
information. Meta-analysis is a statistical procedure which allows combining such results
found in existing literature to produce aggregate effects of demographics and situations on
information needs (Ankem, 2005a). This meta-analysis will show what the results found in
the literature mean when they are mathematically combined.
4. Procedure
To commence the review for this study, online databases – Medline and CINAHL – were
searched to find studies published during the ten-year period 1993–2003 regarding the
information needs of cancer patients. The first two searches were conducted using the
heading bneoplasmQ (with the explode function) and the term bcancer patientsQ (in the title,
abstract, MeSH subject heading, and CAS registry/EC number word). These searches were
combined with the Boolean borQ to retrieve studies dealing with cancer patients. The resulting
search was further combined with other separate searches using the following terms:
information need(s), information seeking, information seeking behavior, information
source(s), and information resource(s) (all in the title, abstract, MeSH subject heading, and
CAS registry/EC number word) to retrieve studies only on the information needs of cancer
patients. The final search thus retrieved was limited to English, adults, and the years 1993–
2003. Additionally, the search in CINAHL was limited to research. The final output of online
searching was 196 studies in Medline and 283 studies in CINAHL.
First, the titles and abstracts of these studies were reviewed, and 110 publications that dealt
with the information needs of cancer patients or the information sources used by cancer
K. Ankem / Library & Information Science Research 28 (2006) 7–23
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patients were selected. (The literature on information source use was retrieved for another
systematic review.) The process of sifting through the literature to find relevant articles
consumes enormous amounts of time. Several criteria are usually established for selecting
individual studies from the retrieved literature. As such, in the next step, the 110 studies were
perused for inclusion based on these predetermined criteria: (1) analysis of information needs
of patients diagnosed with cancer and/or information sources used by cancer patients, (2)
inclusion of adults as subjects in the research study, and (3) application of quantitative
research methods and relevant statistics. Relevant statistics were regarded as those applied to
convey preference for certain types of information or use of information sources and
inferential statistics, in particular, for studying the relationship between demographics (age,
education, and gender) and information needs or information source use and, separately, the
relationship between situations (time since diagnosis, stage of cancer, type of treatment being
received, and a patient’s preferred role in decision-making) and information needs or
information source use.
This led to the elimination of 35 papers that dealt with the following: qualitative
studies on information needs, evaluation of general communication channels or sitespecific cancer information-oriented programs, breast cancer risk, hereditary breast cancer
and genetic testing, prevention and screening, symptoms before diagnosis, information
needs prior to breast biopsy, quality of life during cancer, coping during cancer, and
impact of a particular information provider such as the Cancer Information Service
(CIS).
The next step in a meta-analysis is data abstraction, which is the most tedious part. The
remaining 75 articles were carefully read and evaluated to reach the pool of individual studies
to be included in this meta-analysis. Not only is it required that the same variables are
measured across studies, but the variables must be measured consistently in comparable units
for meaningful amalgamation to take place in a meta-analysis.
At this point, the criteria for selection were (a) examination of a relationship between a
demographic or a situational variable and the overall need for information, (b) use of
inferential statistics, such as t test, chi-square, ANOVA, and correlation for analyzing the
relationships between a demographic or a situational variable and the overall need for
information, and (c) restriction of degrees of freedom to 1 in a chi-square test and in the
numerator of an F test in ANOVA because analyses employing these tests with degrees of
freedom greater than 1 represent unfocused comparisons of many relationships (Rosenthal,
1991). As a result of the review at this stage, articles that served the following purposes were
removed from the pool:
! development of theoretical models of health information seeking, physicians’ opinions
regarding patients’ information needs, or reliability and validity of questionnaires—all of
which are related to methodology;
! focus on physician–patient communication solely, rather than presenting an array of
communication channels between different information sources and patients;
! focus on information provided by a particular information provider (e.g., CIS);
! analysis of readability issues related to information available to patients;
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! analysis of patients’ level of knowledge, patients’ expectations and experiences, need for
educational programs, or unmet needs;
! examination of the format of information presented;
! analysis of patient satisfaction with information received or the effect of satisfaction on
information needs;
! examination of the role of information in decision-making;
! analysis of information needs during breast reconstruction or the information needs of
family members;
! comparison of patients diagnosed with different cancers; and
! examination of the impact of factors on desire for information rather than on need for
information because desire for information has been distinguished from need for
information in the literature.
An effort was also made to separate studies that dealt with information source use and with
the need for specific types of information. Similar to the literature on information source use,
the literature on specific types of information needs was also gathered for a separate
systematic review (Ankem, 2005b). Before data extraction was undertaken, the articles were
evaluated for quality: type of publication, qualifications of authors, rigor in development of
questionnaires administered to gather data, and design of the study. This process led to the
elimination of two more articles due to unclear logical progression and inadequate
explanation and presentation of statistical results that include reports of statistical tests
conducted, test statistics derived, and probabilities reached. The final pool included 12
studies.
All publications in the resulting pool of individual studies were articles that appeared in
peer-reviewed journals. Authors of the individual studies included professionals in various
health-related disciplines: oncology, nursing, psychology, psychiatry, and public health. Also,
each publication included, at least, one author who had advanced degrees—PhD, DNS, or
MD.
The literature provided some evidence to support that individuals with different diagnoses
may have similar information needs (Dediarian, 1987; Graydon et al., 1997). Therefore, no
distinction was made between different types of cancer—breast, gastro-intestinal/colorectal,
hematological, lung, gynecological, urological, skin, CNS, head and neck, unknown primary,
and any others. Individual studies in which researchers recruited subjects diagnosed with any
form of cancer for analyzing the effects of demographics and patient situational variables on
information needs were candidates for the quantitative synthesis in this meta-analysis.
4.1. Measurement of information needs across studies
Consistent measurement of variables that are similar in construct across individual studies
is essential for meaningful quantitative synthesis in a meta-analysis. In measuring information
needs, the dependent variable, although various instruments were used across studies, was
conceptualized as this researcher had predetermined it. Widely used instruments to measure
information needs were the Toronto Information Needs Questionnaire-Breast Cancer (TINQ-
K. Ankem / Library & Information Science Research 28 (2006) 7–23
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BC) (Galloway et al., 1997), Information Subscale of the Health Opinion Survey (Krantz,
Baum, & Wideman, 1980), Patient Learning Needs Scale (PLNS) (Bubela et al., 1990),
Information Styles Questionnaire (Cassileth et al., 1980b), and Information Preference Cards
(Degner & Sloan, 1992). Both reliability and validity of TINQ-BC and PLNS have been
reported. Reliability has been reported for the Information Subscale of the Health Opinion
Survey whereas validity of the Information Styles Questionnaire was tested during its
development.
In one study, a more recent instrument by Degner and colleagues was used, and many more
types of information needs were measured—well beyond information needs related to
diagnosis, treatment, and prognosis which were usually measured in other studies (Davison et
al., 2002). In calculating the overall information need based on data provided in this study for
the meta-analysis, the deviance in the instrument was noted, and the corresponding effect size
representing the effect of an independent variable on the information needs of cancer patients
was removed from the meta-analysis.
Other instruments used were not standardized. These instruments were designed to
measure information needs in the one study for which they were used. Many of these
instruments were adaptations of previous work, such as the seminal work done by Cassileth et
al. (1980b) on the Information Styles Questionnaire and the more recent extensive work done
by Degner and her colleagues on Thurstone scaling of information needs (Bilodeau &
Degner, 1996; Degner & Sloan, 1992; Degner et al., 1997; Luker et al., 1995). These
nonstandardized instruments were either pretested or reported on in sufficient detail in articles
for the researcher to evaluate. Generally, all of the instruments used in individual studies to
measure information needs were administered to subjects who had been recruited from
hospitals and clinics. Also, appropriate methodologies were employed in probing the
questions being sought.
4.2. Measurement of demographic and situational variables across studies
Gender was measured as a categorical variable, male or female. Gender, however, was not
included in the meta-analysis because male and female cancer patients were consistently
found to be no different in information needs. Education and age were measured as either
categorical or continuous variables. The continuous measurements of both education and age
were in number of years. The categorical measurements of education were based either on
grouped number of years of education or on levels of education. Among research studies
which examined the effect of education, the one study that found that cancer patients with
more education wanted more information did not provide all the statistical values necessary
for a meta-analysis. Therefore, education also could not be entered in the meta-analysis. Only
one study measured age as a categorical variable while all other studies measured age as a
continuous variable. Patients in this study were categorized in one of two age groups, V55
and N55, for the analysis of their information needs. If more studies had existed that measured
age categorically, these categories would have had to be similar across studies for meaningful
synthesis of findings to occur. For instance, if two studies apply t test to find differences in
information needs between patients in different age categories, the age categories in the
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studies must be in comparable units for the amalgamation of results in a meta-analysis. For
this meta-analysis, the results that were based on either categorical or continuous
measurements of age were combined.
The measurement of situational variables was more complex. Time since diagnosis was
measured either as a continuous variable in months or as a categorical variable in time
periods. The three major time periods used as categories were diagnosis, treatment, and posttreatment. In some studies, however, patients at more than one point in time within the
treatment phase were compared for analyzing their information needs, which presented a
problem for synthesis. For instance, significant associations in the comparison of information
needs were found between various points in time within the treatment phase and between the
treatment and post-treatment phases. Synthesis could only be accomplished, again, with
similar units across studies. Therefore, analyses across studies of information needs at various
points in time during the treatment phase were included in one subset for the meta-analysis. In
addition, analyses across studies which compared information needs from the treatment phase
to the post-treatment phase were included in a separate subset for the meta-analysis to
decipher the aggregate effect of time on the information needs of cancer patients.
Stage of cancer was measured only categorically based on the predefined stages I, II, III,
and IV of cancer, and the corresponding results were entered. Type of treatment received was
also measured only categorically. The four categories often used in measurement were
curative/radical, palliative, remission/prolong survival, or unknown. Although all were not
covered in each study that examined the effect of the type of treatment received, similar
comparisons across studies would have qualified for entry into the quantitative synthesis.
Another issue in the measurement of type of treatment was that breast cancer treatments were
categorized in far greater detail, for instance, as wedge resection, lumpectomy, partial
mastectomy, and mastectomy. In addition, in some studies, there were separate categories for
lumpectomy and mastectomy in combination with radiotherapy, chemotherapy, or both. All
of these, however, could have been subsumed under a more general treatment category
covered in other studies, and the comparisons would also have qualified for entry into the
meta-analysis, but patients receiving different types of treatment were not found to be
different in their need for information across studies. As such, this situational variable was not
entered in the meta-analysis.
Patient’s preference for a role in illness-related decisions was measured categorically. The
basic categories were active, collaborative, and passive which are based on Degner and
Sloan’s (1992) sorting technique although patients can be and were categorized as simply
active or passive. More detailed categorizations are possible with six categories in total which
represent combinations of the basic three categories. The two studies, where cancer patients
who preferred to be active or some combination of active and collaborative also showed a
greater need for information than those patients who preferred to be passive by leaving all
decisions to the health care system, appeared to have used Degner and Sloan’s sorting
technique in different ways to measure patient preference for participation in treatment. This
discrepancy may cause some variation in measurement because use of Degner and
colleagues’ sorting results in different numbers if participants are categorized differently, in
this case, solely as active as opposed to active–collaborative. Both studies, however, were
K. Ankem / Library & Information Science Research 28 (2006) 7–23
15
included in the meta-analysis under the assumption that the variation was not great, so the
synthesis would still be meaningful.
4.3. Analysis
The Rosenthal and Rubin/Hedges and Olkin approach to meta-analysis was chosen for
running the procedural syntax in Statistical Package for the Social Sciences (SPSS). Details of
the Rosenthal and Rubin/Hedges and Olkin approach, along with those of other meta-analytic
approaches, can be found in another article by this author (Ankem, 2005a). The Rosenthal
and Rubin/Hedges and Olkin approach requires that data for the analysis be entered in the
form of r values which represent relationships between each independent and dependent
variable.
Five subsets of data were established for this meta-analysis to test the aggregate
relationships between:
! age and overall need for information;
! time since diagnosis during treatment and overall need for information;
! time since diagnosis during treatment and post-treatment phases and overall need for
information;
! stage of cancer and overall need for information; and
! patients’ preferred role in treatment-related decisions and overall need for information.
These five aggregate relationships encompass the aggregate effects of the four independent
variables – age, time since diagnosis of cancer, stage of cancer, and patients’ preferred role in
decision-making regarding treatment – on the dependent variable, information needs of
cancer patients. Statistics gathered across studies representing each relationship were entered
into analysis to test the respective aggregate relationship. When results in individual studies
were presented in the form of other inferential statistics, these were converted to r values
using formulas provided by Rosenthal (1991). The r values thus entered for conducting the
meta-analysis are referred to as effect sizes. An effect size essentially indicates the magnitude
of the relationship between an independent and a dependent variable in a meta-analysis.
Researchers reported only significant results, most at P b 0.05 or lower, with all required
statistical test values for entering the test statistics into the meta-analysis; that is, most
researchers did not report test statistics for nonsignificant findings. Therefore, these were
entered as zero correlations, a condition which increases the likelihood of Type II error;
however, there was no other choice.
Also, only one effect size representing a relationship between two variables from a single study
was entered in the meta-analysis. For example, more than one value indicating a relationship
between age and information needs, say at different points during treatment, within a single study
were averaged to obtain one effect size representative of the relationship in that study. Many effect
sizes testing similar hypotheses in a single study may inflate results.
In producing aggregate effect sizes, it is important in a meta-analysis to combine effect sizes
which represent samples from the same population. Therefore, homogeneity tests were
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conducted to gauge any heterogeneity in effect sizes. A test termed the Q test has been
developed for the purpose of detecting heterogeneity of effect sizes in a meta-analysis. A
homogeneity test was conducted for each of the subsets of effect sizes described above in this
meta-analysis. A significant result of a homogeneity test indicates the presence of
heterogeneity. In the presence of heterogeneity, effect sizes are removed, one at a time, until
a nonsignificant result is reached. The syntax for the Q test was run concurrently in SPSS with
the Rosenthal and Rubin/Hedges and Olkin meta-analytic procedure. The findings of the Q
tests, along with other findings, are discussed in the Findings section below.
Another concern in meta-analysis, referred to as the file drawer problem, is related to the
number of possibly existing unpublished studies that could change the outcome of the current
synthesis. A fail-safe number calculated for the meta-analysis which is greater than 5K + 10
(where K is the number of studies included in the meta-analysis) assures the researcher that a
reasonable tolerance level has been achieved (Rosenthal, 1991). For more details regarding
the calculation of fail-safe number, refer to the article by this author on meta-analytic
procedures (Ankem, 2005a).
Due to the existence of several zero correlations in the data for this meta-analysis, a
publication bias favoring those studies which reported significant findings was not an issue in
this meta-analysis, and a fail-safe number was not calculated. The logic behind this is that
most unpublished studies may have nonsignificant findings, and, if a meta-analysis includes
mostly published studies with significant findings, the possibility of the findings of a metaanalysis being overturned by the existence of nonsignificant findings in unpublished studies
increases. However, if the meta-analysis includes several nonsignificant findings, for
example, several zero correlations as this meta-analysis does, the chance that the results of
a meta-analysis will be overturned is reduced.
5. Findings
5.1. Sample homogeneity
Only one of the five subsets of effect sizes established to test the aggregate effects of age,
time since diagnosis, stage of cancer, and patients’ preferred role in treatment decisionmaking on the information needs of cancer patients was found to be heterogeneous. The Q
test for the effect of age on the information needs of cancer patients produced a significant
result at P b 0.05 indicating heterogeneity of effect sizes (v 2 = 13.44, p = 0.04). To achieve
homogeneity of effect sizes in the heterogeneous subset, effect sizes were removed, one at a
time, until a nonsignificant result indicating homogeneity was reached (v 2 = 2.80, p = 0.42).
In the process, three effect sizes in total were identified and removed from the sample of
seven initially entered effect sizes which were abstracted from the seven individual studies to
test the aggregate relationship between age and the information needs of cancer patients
(Feldman-Stewart, Brundage, Nickel, & Mackillop, 2001; Galloway et al., 1997; Iconomou et
al., 2002). Table 1 presents the results of the homogeneity tests conducted for the five subsets
of effect sizes.
K. Ankem / Library & Information Science Research 28 (2006) 7–23
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5.2. Effect sizes
In Table 2, individual effect sizes representing the magnitude of the relationship between
an independent variable and the dependent variable are listed by the study from which they
were abstracted. All effect sizes are presented in the form of r values, including ones that
were converted to r values using formulas provided by Rosenthal (1991).
The results of the meta-analysis which were arrived upon by combining the individual
effect sizes are represented as mean r values and are presented in Table 3. In the
calculation of these aggregate effect sizes, the individual effect sizes were weighted by
their respective sample sizes to give more weight to results from studies with larger
samples. The aggregate effect sizes which represent the aggregate effects of demographics
and patient situations on the information needs of cancer patients are presented in the
following order in Table 3.
(1) mean r representing the aggregate relationship between age and overall information
needs;
(2) mean r representing the aggregate relationship between time since diagnosis during the
treatment phase and overall information needs;
(3) mean r representing the relationship between time since diagnosis during treatment and
post-treatment phases and overall information needs;
(4) mean r representing aggregate relationship between stage of disease and overall
information needs; and
(5) mean r representing aggregate relationship between patients’ preferred role in treatment
decision-making and overall information needs.
Confidence intervals for each of the five aggregate r values were also produced as part of the
SPSS meta-analysis output. The confidence intervals are presented in Table 3 along with the
mean r values. These confidence intervals were examined to determine the significance of each
of the five mean r values. Confidence intervals without a zero indicate that zero correlations are
not possible and that the corresponding mean r values found in the meta-analysis are
significant. More discussions of the results related to each subset of effect sizes follow.
Table 1
Homogeneity of effect sizes
Age and information needs
Time since diagnosis within the treatment phase and information needs
Time since diagnosis during treatment and post-treatment phases and information needs
Stage of cancer and information needs
Preferred role in treatment-related decisions and information needs
v2
p
2.80
4.84
5.32
2.17
1.24
0.42
0.18
0.15
0.34
0.27
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Table 2
Effect sizes representing relationships between demographic and situational variables and information needs
Predictor and study
Age
Graydon et al. (1997), p. 62
Harrison et al. (1999), p. 220
Harrison-Woermke and
Graydon (1993), p. 453
Turk-Charles et al. (1997),
pp. 91–92
Statistic
r = 0.35
r not significant
t = 2.64
r=
0.27
Stage of cancer
Iconomou et al. (2002), p. 318
Harrison et al. (1999), p. 220
Harrison-Woermke and Graydon
(1993), p. 453
70
33
40
DOE
r
–
None
–
0
0.35
0.26
148
–
48
93
100
100
None
None
–
None
0
0
and post-treatment phases
v 2 not significant
100
None
0
ANOVA F no difference
t = 2.53
136
40
None
–
0
v 2 not significant
102
None
0
r = 0.23
t not significant
t not significant
100
33
40
+
None
None
0.23
0
0
+
0.38
Time since diagnosis within the treatment phase
Graydon et al. (1997), p. 63
t not significant
Harrison et al. (1999), p. 220
ANOVA F no difference
Iconomou et al. (2002), p. 318
r = 0.26
Lobb et al. (2001), p. 52
v 2 not significant
Time since diagnosis during treatment
Wong, Stewart, Dancey, Meana,
McAndrews, and Bunston
(2000), p. 16
Harrison et al. (1999), p. 220
Harrison-Woermke and Graydon
(1993), p. 452
Stewart et al. (2000), p. 359
N
Preferred role in treatment-related decisions
Hack et al. (1994),
Need for information on
pp. 284–285
diagnosis, Z = 2.22
Need for information on treatment
alternatives, Z = 3.30
Need for information on treatment
procedures, Z = 2.39
Need for information on treatment
side effects, Z not significant
Need for information on prognosis,
Z not significant
35
0.27
0.26
0
0.38
0.56
0.40
0
0
Average
r = 0.27
Davidson, Brundage, and
Feldman-Stewart (1999),
p. 516
v 2 = 6.08
21
+
0.54
K. Ankem / Library & Information Science Research 28 (2006) 7–23
19
Table 3
Pooled effect sizes
Subsets of effects sizes weighted by sample size
Age and information needs
Time since diagnosis within the treatment phase
and information needs
Time since diagnosis during treatment and
post-treatment phases and information needs
Stage of cancer and information needs
Preferred role in treatment-related decisions and information needs
Mean r
CI
0.26
0.08
0.37, 0.15
0.21, 0.06
0.06
0.20, 0.08
0.13
0.38
0.03, 0.29
0.09, 0.61
5.3. Age and overall need for information
A moderate aggregate relationship, represented by a mean r = 0.26, between age and
overall need for information among cancer patients was found (Table 3). The aggregate effect
size was significant as zero was not within the 95% confidence interval ( 0.37, 0.15). The
negative direction of the effect size indicates that the younger the cancer patients were, the
greater was their need for information.
5.4. Time since diagnosis and overall need for information
Two aggregate relationships were tested concerning the effect of time since a patient’s
diagnosis of cancer on his or her information needs. The first, the aggregate relationship
(mean r = 0.08) found between time since diagnosis within the treatment phase and
information needs of patients, was nonsignificant (Table 3). Zero was within the 95%
confidence interval ( 0.21, 0.06). The finding suggests that there is no difference in the
information needs of cancer patients at different points in time during their treatment.
Therefore, patients do need information, but the need is constant as time passes during
treatment.
The second, the aggregate relationship (mean r = 0.06) found between time since
diagnosis from the treatment phase to the post-treatment phase and information needs, was
also nonsignificant (Table 3). Zero, again, was within the 95% confidence interval ( 0.20,
0.08). This result shows that there was no difference in cancer patients information needs, not
only during treatment, but also as they moved from treatment to post-treatment phases of their
illness. In summary, information needs were constant as these patients had new experiences
and needed information at every step, rather than a tapering need for information as
familiarity with cancer increased with time among these patients.
5.5. Stage of cancer and overall need for information
The aggregate relationship (mean r = 0.13) found between stage of cancer and overall need
for information was nonsignificant (Table 3). Zero was in the 95% confidence interval
( 0.03, 0.29). The finding implies that cancer patients at all stages of cancer need
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K. Ankem / Library & Information Science Research 28 (2006) 7–23
information and that patients at advanced stages of cancer were no different from patients at
an early stage of cancer in how much information they wanted.
5.6. Preferred role in making treatment-related decisions and overall need for information
A mean r = 0.38, showing a moderate aggregate relationship between a patient’s
preference to be actively involved in treatment-related decisions and the level of patient’s
information needs, was found to be significant (Table 3). Zero was not within the 95%
confidence interval (0.09, 0.61). The meta-analytic finding supports that preference for an
increased role in illness-related decisions by cancer patients is associated with increased need
for cancer-related information.
6. Discussion
To give more meaning to the meta-analytic finding that younger patients need more
information, cancer patients’ ages in the studies that were included in the meta-analysis were
examined. Patients’ ages in the studies ranged from 19 to over 75. The one study in which
cancer patients were categorized to see if their information needs were different, grouped
patients based on whether they were V55 years of age or N55 years of age. In exploring other
age-related questions, this grouping seemed to be the norm in the literature. In addition, the
average age of cancer patients was in the mid-fifties in three of the four studies. Only one
reported an average age of 49.7. Hence, in giving a more precise meaning to the notion of
younger versus older cancer patients who need more information, it is reasoned here that
cancer patients who are V55 years in age need more information compared to those over 55
years in age.
Different explanations have been provided in the literature for the difference in information
needs based on patient’s age (Turk-Charles et al., 1997). One explanation is that these patients
may be accustomed to a different way of socializing with physicians based on their
interactions from an earlier time period. Another explanation is that older patients may simply
have different coping styles. These two explanations have different implications for
information provision. If the first explanation is true, strategies are required to make older
patients feel more comfortable so that they will expect more information and ask more
questions. If the latter explanation is true, younger people may need more information to cope
and older patients may be overwhelmed by information, and this difference must be
considered in information provision.
Social exchange theory offers yet another explanation which is also related to the
social context of the elderly similar to one of the explanations presented in the previous
paragraph (Bengtson, Burgess, & Parrott, 1997; Lynott & Lynott, 1996). According to the
social exchange theory, the elderly are selective in their interactions with people,
withdrawing from some people while increasing contact with others where emotional
support rather than information is sought in reciprocity. As such, the elderly still need
information, although less than younger patients, and they seek information but from
K. Ankem / Library & Information Science Research 28 (2006) 7–23
21
sources other than the ones used by younger patients. Turk-Charles et al. (1997), when
they observed information seeking from nonmedical sources separately, found that
information seeking from these sources, including interpersonal as well as mass media,
did not differ between older and younger patients (information seeking from medical and
nonmedical sources were combined for the meta-analysis which produced higher
information needs on the part of younger patients).
The postulations in the literature point to different directions for those interested in
designing interventions. Therefore, the reasoning behind older cancer patients’ lower need for
information calls for more research, including a probe of sources preferred for seeking
information by younger versus older patients.
Beyond age, patients who wanted to actively participate in illness-related decisions were
found in the meta-analysis to also need more information. Interestingly, this association seems
to be stronger over time. An earlier study by Hack et al. (1994) found a moderate correlation,
and the more recent study by Davidson et al. (1999) reported a higher correlation. This
increase in the magnitude of the relationship may indicate that cancer patients who prefer to
be active in taking part in treatment decisions have even greater need for information about
their illness.
7. Conclusion
This meta-analysis shows younger cancer patients and those cancer patients
preferring active involvement in treatment-related decisions to need more information.
Although more research is called for in the area, with clearly defined concepts,
adequate reporting of statistical results, and standardized measurement, the findings
from this meta-analysis can still be used to guide information provision. The latter
finding may be more relevant to the health care system and family and friends who
can evaluate a patient’s preference for an increased role in illness-related decisions and
provide information accordingly. The first finding, which is based on age, is relevant
to all contexts where health information is transferred, whether in hospitals or in
information centers, where one must consider patients’ characteristics and cater to their
information needs.
At present, the reasons behind the lower need for information among older cancer patients
must be probed further during an information exchange until more research is conducted, to
see if the older patient is constrained by the social context rather than by the fear of being
overwhelmed with information. If the patient is constrained by the social context, efforts must
be made to make these patients feel more comfortable in seeking information.
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