An In-depth Exploration of Information-Seeking Behavior Among Individuals With Cancer
Bibliographic record
Abstract
This is the second of a 2-part article describing differential health information-seeking behavior (HISB) patterns within the context of a cancer diagnosis that emerged in our grounded theory study. Data from 30 semistructured interviews and 8 focus groups with individuals diagnosed with breast, prostate, or colorectal cancer were analyzed using constant comparison analysis, diagramming, and open, axial, and selective coding. In part 1, 3 HISB patterns illustrating variation in active information-seeking behavior were described: (1) intense information seeking a keen interest in detailed cancer information, (2) complementary information seeking the process of getting "good enough" cancer information, and (3) fortuitous information seeking the search for cancer information mainly from others diagnosed with cancer. Part 2 describes 2 additional patterns coined in this study as minimal information-seeking behavior limited interest for cancer information and guarded information-seeking behavior avoidance of certain types of cancer information. Part 2 challenges traditional views that consider disinterest and avoidance as similar concepts subsumed under "blunting." Findings may be used to refine informational interventions and measurement strategies to best differentiate between cancer information avoidance and disinterest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 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".