Looking Beyond the Internet: Examining Socioeconomic Inequalities in Cancer Information Seeking Among Cancer Patients
Bibliographic record
Abstract
The gap in cancer information seeking between high-socioeconomic-status (high-SES) cancer patients and low-SES cancer patients deserves serious attention, considering the importance of information and knowledge in cancer control. We thus explored the association of SES, as measured by education, with cancer patients' overall cancer information seeking, and with seeking from each source (i.e., the Internet, mass media, medical sources, and nonmedical interpersonal sources) and across two topic categories (i.e., treatment, quality of life). We then asked whether the effect of education on treatment information seeking is reduced among those who are particularly motivated to control treatment choices. We conducted a survey with breast, prostate, and colon cancer patients diagnosed in 2005 (n = 2,013), who were randomly drawn from the Pennsylvania Cancer Registry in the fall of 2006. We found that education was more strongly associated with Internet use than with the use of other sources regardless of topics. Also, when information was sought from mass media, education had a greater association with treatment information seeking than with quality-of-life information seeking. Preference for active participation in treatment decision making, however, did not moderate the effect of education on treatment information seeking. The implications of these findings for public health research and cancer patient education were discussed.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".