Systematic review of the role of gender as a health determinant of hospitalization for depression
Bibliographic record
Abstract
OBJECTIVES: To conduct a systematic review of selected health determinants, including gender, and their impact on hospitalization rates for depression. Depression includes both depressive and bipolar disorders. Selected health determinants were gender, age, sex, family structure, education, and socioeconomic status. METHODS: Systematic search of conventional and fugitive literature sources. All reports of primary data, systematic reviews, and meta-analysis of primary data were included if they focused on hospitalization for depression and reported data by one or more of the selected health determinants. Two researchers independently evaluated each citation for inclusion and extracted data from the included studies. RESULTS: There is an important underreporting of health determinants data in studies of hospitalization for depression. No studies examined the role of gender. Age and sex were reported in 83 percent and 80 percent of the 110 included studies. Women showed a higher rate of hospitalization for depression than men (p < .05). Age and diagnosis had different effects in men and women. Adult women were significantly more likely than men to report a depressive disorder, whereas men were more likely to report a bipolar disorder (p < .05). Little can be concluded on the other health determinants. CONCLUSIONS: The importance of reporting hospitalization data and conducting hospital utilization analysis by sex and health determinants, including gender, must be emphasized.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".