Global absenteeism and presenteeism in mental health patients referred through primary care
Bibliographic record
Abstract
BACKGROUND: Disability from mental health (MH) symptoms impairs workers' functioning. Most of what is known about the MH of workers relates to their experiences after intervention or work absence. OBJECTIVE: To profile the clinical symptoms, self-reported absenteeism and presenteeism and treatment response of workers with MH symptoms at the point of accessing MH care and compare the characteristics of patients referred with or without problems related to work. METHODS: Analysis of 11 years of patient data collected in a Shared Mental Health Care (SMHC) clinic referred within a primary care setting in Ontario, Canada. Multiple regression with MH disorders was used to predict absenteeism and presenteeism. Absenteeism and presenteeism were assessed using the 12-item self-administered version of the WHO-DAS 2. Symptom profiles were assessed with the Patient Health Questionnaire (PHQ). RESULTS: Some psychiatric disorders (depression, somatization, anxiety) contributed more to predicting absenteeism and presenteeism than others. Patients referred with work-related problems differed from the general SMHC population in terms of sex and type and number of symptoms. Treatment response was good in both groups after a mean of three treatment visits. CONCLUSIONS: Patients with work-related mental health complaints formed a distinct clinical group that benefitted equally from the intervention(s) provided by SMHC.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".