Determinants of breast cancer knowledge among newly diagnosed, low‐income, medically underserved women with breast cancer
Bibliographic record
Abstract
BACKGROUND: Among women with breast cancer (BC), greater BC knowledge has been associated with greater participation in treatment decision-making, patient satisfaction, and survival. The objective of this study was to identify modifiable determinants associated with BC knowledge. METHODS: Data were collected from a telephone survey of medically underserved women with BC in California (n = 909). The dependent variable for analysis was BC knowledge. The modifiable determinants that were assessed included 1) physician-patient discussion of BC topics, 2) receipt of written BC-related material, 3) self-efficacy in interacting with physicians, 4) physician emotional support, 5) discussions with a BC survivor, and 6) office visit support by relatives/friends. Multivariate linear regression was used to examine the effect of those determinants on BC knowledge while controlling for socioeconomic factors, clinical characteristics, and treatment received. RESULTS: The average knowledge score was 6.9 (standard deviation, 2.3; range, 0-10). In multivariate analyses among women with less physician emotional support, those with the greatest self-efficacy had higher knowledge scores than those with the least self-efficacy (8.2 vs 5.4; P < .001). For women with low self-efficacy, those with more physician emotional support had higher knowledge scores than those with less physician emotional support when the analysis was controlled for confounding factors (6.3 vs 5.4; P < .001); physician information-giving had no effect on BC knowledge. CONCLUSIONS: The study findings suggested significant associations of patient self-efficacy and physician emotional support with BC knowledge; physician emotional support appeared to be more important than physician informational support. Further research will be needed to investigate whether interventions that target these 2 domains may be effective in increasing BC knowledge in disadvantaged populations.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".