Cancer information-seeking profiles: A self-report measure of patients’ distinct preferences for information about their cancer
Bibliographic record
Abstract
Objective: Contemporary cancer care approaches, such as value- and strengths-based health care, involve integrating patients' needs and preferences, as the cancer experience unfolds. Distinct cancer information-seeking behaviours among patients are increasingly found to influence patient-provider communication, treatment-related decision-making, health processes and outcomes, as well as patient satisfaction with cancer care services. My program of research has already documented diverse cancer information-seeking profiles (CISP) among patients. These profiles can easily be captured by a theory-driven and empirically sound self-report single-item measure presented herein. Methods: Guided by Self-Evaluation Theory (SET) - which posits that individuals vary in their motivation to seek information about the self - quantitative work established that SET motives could be reproduced in health-related contexts (N = 600). Qualitative inquiry subsequently explored information-seeking among individuals with various cancer diagnoses. Based on this series of studies, a CIPS measure (containing a general question which prompts individuals to choose the most representative statement based on five descriptions of distinct informational profiles) was tested in a large sample of individuals with diverse cancers. Results: In this latter sample (N = 2,142), patients were administered the CISP and the Ambulatory Oncology Patient Satisfaction Survey (AOPSS). The most frequently reported CISP was complementary (50.2%, i.e., search for cancer information that added to what patients already knew), followed by minimal (25.2%, i.e., no search for cancer information), then guarded (14.4%, i.e., avoiding negative and only seeking hopeful cancer information), peer-focused (6.4%, i.e., search for cancer information from similar others), and last, intense (3.8%, i.e., seeking as much cancer information as possible). These profiles were also differentially related to care satisfaction with intense profiles being the least satisfied across all cancer care domains. Conclusions and Practice Implications: The CISP measure can readily assess patients' profiles in the search for (or avoidance of) information about their cancer so that more personalized interventions can be provided. Future research exploring how CISP evolves, as the cancer experience unfolds, is also worthy of further study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".