Stress-buffering effect of social support on immunity and infectious risk during chemotherapy for breast cancer
Bibliographic record
Abstract
OBJECTIVE: This study investigated the stress-buffering effect of social support on immune function and infectious risk in women with breast cancer, during and after chemotherapy. METHOD: Data were collected from 50 women with breast cancer before and after their chemotherapy, as well as three months later. Stress was measured by daily hassles related to cancer and social support by marital status (MS) and perceived support from friends (Ps-fr). Blood was collected to measure innate immune markers (i.e., T cells, NK cells and neutrophils). Infections were evaluated using a semi-structured interview. Moderation, mediation and moderated mediation models were computed to test the hypotheses. RESULTS: Higher stress at baseline was found to significantly predict a higher occurrence of infections during chemotherapy, but not three months later. The relationship between stress and infections was not significantly explained by any of the immune markers. The interaction between stress and social support was tested using MS alone and combined with Ps-fr. A protective effect of social support on the deleterious effect of stress on infectious risk was found. Single patients reporting lower Ps-fr showed the strongest association between stress and infections, while the weakest association was found in patients in a committed relationship with a higher level of Ps-fr. CONCLUSIONS: Women experiencing more stress before the beginning of chemotherapy would appear to be at a higher risk of developing infections during their treatment. Results of this study also suggest that this effect could be buffered by the presence of a romantic partner and by higher Ps-fr.
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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 | 0.004 |
| 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".