The roles of negative affect and goal adjustment capacities in breast cancer survivors: Associations with physical activity and diurnal cortisol secretion.
Bibliographic record
Abstract
OBJECTIVE: This study examined whether within-person changes of breast cancer survivors' high-arousal negative affect (e.g., feeling scared, upset, anxious, or guilty) could predict high levels of diurnal cortisol secretion and moderate-to-vigorous physical activity (MVPA). In addition, goal adjustment capacities (goal disengagement and goal reengagement) were expected to buffer the effect of negative affect on cortisol and to increase its effect on MVPA. METHOD: High-arousal negative affect, self-reported MVPA, area-under-the-curve of diurnal cortisol secretion, and goal adjustment capacities were assessed in a longitudinal sample of 145 female breast cancer survivors. RESULTS: Based on hierarchical linear modeling, breast cancer survivors reported increased levels of both MVPA and cortisol secretion if they experienced higher (as compared with lower) levels of high-arousal negative affect than their personal average. Furthermore, within-person negative affect was associated with: (a) higher MVPA among participants with high (but not low) goal reengagement capacities; and (b) elevated cortisol secretion among participants with low (but not high) goal reengagement capacities. CONCLUSIONS: High-arousal negative affect may exert differing functions among breast cancer survivors in that it can trigger adaptive health behaviors, yet simultaneously elevate diurnal cortisol secretion. In addition, being able to engage in new goals may be a necessary condition for breast cancer survivors to experience the beneficial behavioral effects of high-arousal negative affect, and it may prevent the adverse effect of negative affect on enhanced cortisol output. (PsycINFO Database Record
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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 | 0.002 |
| 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) | 0.001 | 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".