SS04-02 FREQUENCY OF MENTAL HEALTH PROBLEMS AT WORK OVER TIME AND DESIGN OF WORKPLACE PHYSICAL AND MENTAL CONDITIONS
Bibliographic record
Abstract
Abstract Introduction Numerous population-based studies have been conducted on the factors that contribute to mental health problems in the workplace and the factors that promote well-being in the workplace. However, knowledge of these factors does not mean that best practices or interventions will automatically be implemented in the workplace. Methods The Observatory on Mental Health and Well-being at Work (OSMET) conducted a large longitudinal study of 6602 people in 95 workplaces that participated in the OSMET Longitudinal Study (ELOSMET). This is an online survey started in 2019 that is repeated every year for five years. We will report the results of the first three years taking into account those obtained before, during, and following the intensive COVID-19 period. Results The frequency of nervous breakdown problems increased during COVID-19 in age groups over 35 years (14% to 17%) while the frequency of burnout decreased significantly (29.7% to 25.1%). Moreover, following this pandemic, it seems that the frequencies of these same problems are returning to their original level. Conclusions In these workplaces, in age groups over 35 years of age, we are seeing problems returning to work under the same conditions as before COVID-19. It is becoming clear, as we have already pointed out in previous studies, that the solutions involve a critical examination of jobs and working conditions and a new design of these.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".