Perceived stress and its socio-demographic predictorsin Iranian women receiving treatment for breast cancer
Bibliographic record
Abstract
Search, G -Funds CollectionBackground.Due to pathological differences with other chronic diseases, breast cancer causes psychological and emotional problems, such as negative feelings, anxiety and stress.Objectives.The present study aimed to measure the perceived level of stress and its socio-demographic predictors among women with breast cancer.Material and methods.This cross-sectional study was conducted on 166 women receiving treatment for breast cancer in Ghazi Tabatabaie, Al-Zahra, Vali-Asr and Shams hospitals of Tabriz-Iran in 2017.A convenience sampling method was employed to select the participants.The required data was gathered using Cohen's Perceived Stress Scale (PSS) and then statistically analysed using the independent t-Test, one-way ANOVA, the Pearson correlation coefficient and multivariate linear regression analysis.Results.The mean (standard deviation) score of perceived stress was 32.9 (5.2) out of 56.The multivariate linear regression analysis showed that income, education of the mother and marital satisfaction were predictors of perceived stress.These variables predicted 21.5% of the observed variance in the total score of perceived stress. Conclusions.The results indicated that the mean score of perceived stress was higher than average in women receiving treatment for breast cancer.Therefore, it is necessary to develop and execute strategies to reduce stress in such patients, especially those with a family history of breast cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".