Do behavioural self-blame and stigma predict positive health changes in survivors of lung or head and neck cancers?
Bibliographic record
Abstract
UNLABELLED: Survivors of lung or head and neck cancers often change tobacco and alcohol consumption after diagnosis, but few studies have examined other positive health changes (PHCs) or their determinants in these groups. The present study aims to: (a) document PHCs in survivors of lung (n = 107) or head and neck cancers (n = 99) and (b) examine behavioural self-blame and stigma as determinants of PHCs. We hypothesised that: (a) survivors would make a variety of PHCs; (b) behavioural self-blame for the disease would positively predict making PHCs; and (c) stigma would negatively predict making PHCs. METHODS: Respondents self-administered measures of PHC, behavioural self-blame, and stigma. Hierarchical multiple regression analysis tested the hypotheses. RESULTS: More than 65% of respondents reported making PHCs, the most common being changes in diet (25%), exercise (23%) and tobacco consumption (16.5%). Behavioural self-blame significantly predicted PHCs but stigma did not. However, both behavioural self-blame and stigma significantly predicted changes in tobacco consumption. CONCLUSIONS: Many survivors of lung or head and neck cancers engage in PHCs, but those who do not attribute the disease to their behaviour are less likely to do so. Attention to this problem and additional counselling may help people to adopt PHCs.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".