An In-depth Exploration of Information-Seeking Behavior Among Individuals With Cancer
Bibliographic record
Abstract
The purpose of this 2-part paper was to describe individuals' health information-seeking behavior (HISB) patterns that emerged from our grounded theory study. Thirty individual interviews and 8 focus groups were conducted with individuals diagnosed with cancer. Analysis was characterized by constant comparison diagram, an evolving coding scheme, and ultimately the generation of a grounded theory of HISB patterns. Five HISB patterns were identified: (1) intense information seeking-a keen interest in detailed cancer information; (2) complementary information seeking--the process of getting "good enough" cancer information; (3) fortuitous information seeking--the search for cancer information mainly from others diagnosed with cancer; (4) minimal information seeking--a limited interest for cancer information; and (5) guarded information seeking--the avoidance of some cancer information. Part 1 focuses on describing the first 3 HISB patterns considered to illustrate variations in active information seeking. Each pattern is explained, including the type, amount, and sources of information sought. This analysis documents variations in active HISB often overlooked in the cancer literature. Findings may assist healthcare professionals in tailoring their informational interventions according to a patient's preferred HISB pattern. Furthermore, findings may inform the refinement of instruments measuring HISB to include variations in active information seeking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".