Stress Reactivity, Health Behaviors, and Compliance to Medical Care in Breast Cancer Survivors
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To explore relationships among quality of life (QOL), stress reactivity, health behaviors, and compliance to medical care in breast cancer survivors. DESIGN: One-time descriptive laboratory study. SETTING: A visual motor laboratory at a rural university in the southeastern United States. SAMPLE: 25 breast cancer survivors. METHODS: Participants were subjected to the Trier Social Stress Test (TSST) in a laboratory setting and completed questionnaires at home prior to and after the laboratory session. main research variables: Changes in heart rate variability (HRV), salivary cortisol, and state anxiety from the State-Trait Anxiety Inventory (STAI) estimated stress reactivity. Health behaviors, QOL, and trait anxiety were determined by questionnaires. Compliance to medical care was determined from medical records. FINDINGS: Analyses of variance (ANOVAs) indicated that QOL scores were higher for participants with lower compared to higher stress reactivity (p < 0.05). In addition, ANOVAs revealed that participants high in compliance to medical care indicated a lower stress response as determined by HRV (p < 0.01) and the STAI (p < 0.05) compared to those low in compliance. No significant differences were noted in any of the health behaviors based on stress reactivity. CONCLUSIONS: The data suggest that breast cancer survivors who indicate the greatest stress reactivity tend to have the poorest compliance to medical care and lowest QOL. IMPLICATIONS FOR NURSING: Nurses may wish to provide additional support to breast cancer survivors who indicate high stress reactivity in the hopes of improving compliance to medical care and QOL. KNOWLEDGE TRANSLATION: The data suggest that supportive care strategies that reduce stress could potentially improve compliance to medical care in breast cancer survivors. In addition, strategies for managing stress may result in improvements in QOL. Health behaviors, according to the data, do not seem to be influenced by stress reactivity.
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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".