Psychological well-being of rural left-behind women in Northwest China and its associated factors: a regional, population-based study
Bibliographic record
Abstract
Purpose: Growing awareness has highlighted the challenging living condition faced by rural left-behind women (RLW), yet their psychological well-being has not been fully investigated. This study aims to investigate the psychological well-being of RLW in Northwest China and exploring its associated factors. Samples and methods: A total of 697 RLW from five provincial regions were enrolled. Sociodemographic characteristics were collected using a set of researcher-designed questionnaires. Depression, anxiety, and feeling of security were assessed using the Zung's self-rating depression scale (SDS), Zung's self-rating anxiety scale (SAS), and security questionnaire (SQ), respectively. Results: The prevalences of depression and anxiety among RLW were 35.7 and 37.6%, respectively, and feelings of security was relatively low in RLW, with a mean SQ score of 50.16 ± 11.37. Chi-square tests and multiple linear regression analyses indicated that labor intensity, physical health conditions, marital satisfaction and stability, relationships with children, frequency of husband coming home, left-behind duration, domestic violence, and sexual harassment after husbands left were risk factors of psychological well-being of RLW. Conclusion: These findings revealed that the psychological well-being of RLW in Northwest China is not promising, which should therefore, be given special attention. It is essential to prioritize the improvement of the psychological well-being for RLW by providing accessible and targeted supports and interventions tailored to cope with their challenges.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".