SEX DIFFERENCES IN MENTAL HEALTH OF OLDER PEOPLE
Bibliographic record
Abstract
This study examines the differences in mental health between older men and women. A cross-sectional telephone survey was conducted with a representative sample of 504 older adults aged 55 years and older. Mental health was measured by a revised Chinese version of the Geriatric Depression Scale and the Mental Component Summary (MCS) of a Chinese version of SF-36. Mental health differences between men and women were not identified, but gender was found to have an effect on mental health in the 54 to 64 age group, when being a female predicted a poorer status of mental health. The poorer financial status of women was the reason for the gender effect. Mental health predictors for men and women were also compared. The findings concluded that interventions to strengthen financial assistance for aging women were important for enhancing mental health. 這研究對五百零四名五十五歲或以上的長者進行電話調查。結果發現男、女長者精神健康差別在五十五至六十四歲的組別中顯示，較差的經濟狀況是影響女性精神健康的因素。比較男、女精神健康指標的結果顯示，經濟援助對強化女性精神健康的重要性。.
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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) | 0.002 | 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".