Physical, psychological and demographic factors associated with military discharge: a systematic review
Bibliographic record
Abstract
Abstract Aims: The present study is a review focused on analyzing the physical, psychological, and demographic factors that lead recruits to be dismissed or to request their dismissal during basic military training periods. Methods: This study is a systematic review of cohort studies. The following databases were searched in June 2019 and updated in July 2020: Embase, LILACS, CINAHL, Cochrane, MEDLINE, SCOPUS, SPORTDiscus, Web of Science, and Science Direct databases. The MeSH descriptors military personnel, risk factors, and discharge were used to elaborate the search equations. Reference lists were explored to find studies that examined the association between physical, psychological, and demographic factors that lead recruits to be discharged. The following data were extracted from the studies: profile of the participants, sample size, type of risk factors, the duration of follow-up, and the results of the statistical analysis carried out in the studies included. The risk of bias was analyzed with the Newcastle-Ottawa Scale for cohort studies. Results: A total of 531 titles were retrieved from the databases, and eight articles met the eligibility criteria. The results showed the factors associated with discharge, in descending order: musculoskeletal injuries and other medical questions, depressive and behavioural disorders, performance in physical fitness tests, and others. Factors such as educational level, alcohol use, history of suicide attempt, and imprisonments were not associated with an increased risk of being discharged. Conclusion: Musculoskeletal injuries, depression, running performance, previous physical exercise practice, and demographic factors were associated with an increased risk of being discharged.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".