Can you un-ring the bell? A qualitative study of how affect influences cancer screening decisions
Bibliographic record
Abstract
BACKGROUND: The belief that early detection is the best protection against cancer underlies cancer screening. Emerging research now suggests harms associated with early detection may sometimes outweigh the benefits. Governments, cancer agencies, and organizations that publish screening guidelines have found it is difficult to "un-ring the bell" on the message that "early detection is your best protection" because of its widespread communication and enduring resonance. This study explores affective factors-and their interplay with relevant analytical factors-in public/laypersons' decision making about cancer screening. METHODS: A total of 93 people (47 men, 46 women) attended focus groups about, respectively, prostate cancer screening and breast cancer screening in two Canadian cities. RESULTS: Affective factors were a major influence on many focus group participants' decision making about cancer screening, including fear of cancer and a generalized enthusiasm for prevention/screening, and they were often inspired by anecdotes about the cancer experiences of family and friends. Affect also existed alongside more analytical factors including assessments of reduced risk in the management of any cancer diagnosis if caught early, and, for men, the belief that an unreliable test is "better than nothing," and that men deserve prostate cancer screening because women have breast and cervical cancer screening. Affective factors were particularly noticeable in the sub-groups most supportive of screening and the "early detection" message: older women who felt that mammogram screening should begin at age 40 rather than 50, and older men who felt that prostate cancer screening should be expanded beyond its current unorganized, opportunistic usage. In contrast, younger participants displayed less affective attachments to "early detection" messages and had greater concerns about harms of screening and were more receptive to nuanced messages informed by evidence. CONCLUSION: Policymakers attempting to communicate more nuanced versions of the "early detection" message need to understand the role of affect alongside other judgments brought into laypersons' decision making processes and anticipate how affective responses to their messages will be shaped, transformed, and potentially subverted by external forces beyond their control. Particularly overt external factors are campaigns by cancer advocacy organizations actively promoting breast and prostate cancer awareness and screening to younger women and men using affectively-charged messages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".