Organizational predictors and health consequences of changes in burnout: A 12‐year cohort study
Bibliographic record
Abstract
Summary We investigated job burnout and job characteristics, including decision authority, skill discretion, predictability, and information flow, among Finnish forestry workers ( N = 4356) in a longitudinal study. We linked these responses individually with data on the participants' subsequent prescriptions for psychotropic drugs including antidepressants. We aim to study the antecedents of changes in burnout levels over four years time and their health‐related consequences in an eight‐year follow‐up. The results showed that inconsistency among the levels of the Maslach Burnout Inventory subscales (e. g., high scores in exhaustion and low cynicism or vice versa) at baseline identified patterns that were prone to change in burnout four years later. Information flow predicted the direction of this change for the exhaustion and cynicism aspects of burnout, whereas skill discretion and predictability did so for reduced professional efficacy. Change toward burnout predicted future risk of psychotropic drug use. It seems that adverse changes in burnout are influenced by poor organizational resources, and change toward burnout is likely to elevate the risk of poor mental health. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".