Examining social class as it relates to heuristics women use to determine the trustworthiness of information regarding the link between alcohol and breast cancer risk
Bibliographic record
Abstract
BACKGROUND: High rates of alcohol consumption by midlife women, despite the documented risks associated with breast cancer, varies according to social class. However, we know little about how to develop equitable messaging regarding breast cancer prevention that takes into consideration class differences in the receipt and use of such information. OBJECTIVE: To explore the heuristics used by women with different (inequitable) life chances to determine the trustworthiness of information regarding alcohol as a modifiable risk factor for breast cancer risk. METHODS AND MATERIALS: Interviews were conducted with 50 midlife (aged 45-64) women living in South Australia, diversified by self-reported alcohol consumption and social class. Women were asked to describe where they sought health information, how they accessed information specific to breast cancer risk as it relates to alcohol, and how they determined whether (or not) such information was trustworthy. De-identified transcripts were analysed following a three-step progressive method with the aim of identifying how women of varying life chances determine the trustworthiness of alcohol and breast cancer risk information. Three heuristics were used by women: (1) consideration of whose interests are being served; (2) engagement with 'common sense'; and (3) evaluating the credibility of the message and messenger. Embedded within each heuristic are notable class-based distinctions. CONCLUSIONS: More equitable provision of cancer prevention messaging might consider how social class shapes the reception and acceptance of risk information. Class should be considered in the development and tailoring of messages as the trustworthiness of organizations behind public health messaging cannot be assumed.
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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 | 0.009 | 0.045 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".