Anxiety and contradictory class position in the hierarchy of Brazilian firefighters
Bibliographic record
Abstract
BACKGROUND: Workers holding intermediate hierarchical positions in an institution may have a higher risk of occupational stress-related, ill health. This study examined the prevalence rates and odds ratios (ORs) of anxiety disorders among a hierarchical group of firefighters. METHODS: This cross-sectional study samples firefighters from Minas Gerais, Brazil, who answered a structured questionnaire in 2011 (survey completion rate = 89.5%). The outcome of interest was a medical diagnosis of anxiety disorder. Bivariate and multivariate analyses were conducted among five hierarchical occupational positions: privates (lowest position), corporals, sergeants (intermediate position), sub lieutenants, and officers (highest position). RESULTS: Overall, 8.4% of the sample reported an anxiety disorder, with the highest rate observed among intermediate workers (sergeants = 14.2%), followed by corporals (10%), privates (5.6%), sub lieutenants (5%), and officers (2.1%). Compared with privates, the unadjusted OR for sergeants was 2.49 (95% confidence interval, 1.35, 4.58). This finding remained statistically significant after adjustment for several control variables but was eliminated by age. CONCLUSION: The mental health of firefighters is affected by social class position. Mental health promotion efforts should focus on longitudinal research and work toward interventions aimed at modifying the hierarchical structure of workplaces.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".