“Why me?” – women’s use of spiritual causal attributions in making sense of breast cancer
Bibliographic record
Abstract
OBJECTIVE: This study addressed the role of positive (event is due to God's Love or to God's Will) and negative (event is due to God's Anger) spiritual causal attributions in women's adjustment to breast cancer. DESIGN: Ninety-three women diagnosed with breast cancer were assessed at six times from pre-diagnosis through two years post-surgery. MAIN OUTCOME MEASURES: Women completed positive and negative measures of spiritual causal attributions (e.g. God's Love), cognitive appraisals (e.g. threat), coping behaviour (e.g. avoidance) and well-being (e.g. distress). RESULTS: Positive spiritual attributions were consistently related to positive aspects of adjustment (e.g. positive appraisal, acceptance coping, and/or emotional well-being) while negative spiritual attribution was related to negative factors (e.g. appraisals of loss and uncontrollability, avoidance coping, and/or emotional distress). Path analyses revealed that the effects of positive and negative spiritual attributions on well-being were mediated by general cognitive appraisal and coping behaviour. Cross-lagged correlational analysis revealed a 'downward spiral' effect wherein the negative attribution of God's Anger at pre-diagnosis predicted greater distress at 1 week pre-surgery which in turn predicted an increase in the negative attribution and so on across time. CONCLUSION: Although positive spiritual attributions may help women maintain an attitude of hope and acceptance in the face of cancer, results indicate that the effects of negative spiritual attribution can play a significant role in undermining their well-being.
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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 | 0.002 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".