Tendências e diversidade na utilização empírica do Modelo Demanda-Controle de Karasek (estresse no trabalho): uma revisão sistemática
Bibliographic record
Abstract
INTRODUCTION: Karasek's demand-control model has been used to investigate association between job strain and health outcomes. However, different instruments and definitions have been utilized to assess the exposure 'high strain at work', which makes difficult the comparison of results across studies. OBJECTIVE: To describe the measurement instruments and the definitions adopted for the exposure variable 'job strain', according to the demand-control model, by observational studies published until 2010. METHODS: Systematic review of observational studies published until December 2010, addressing the exposure 'job strain', measured according to the demand-control model and used the JCQ or its derivatives, since explicit. RESULTS: Among 877 selected abstracts, 496 (57%) met the inclusion criteria. It identified a trend towards the increasing production literature on the subject. Most studies were sectional; found no relevant differences among study populations of men and women. Sweden, USA, Japan and Canada accounted for 57% of publications, mostly including more than 1000 participants and diverse occupations. Cardiovascular outcomes and their risk factors were the most studied (45%), followed by those related to mental health (25%). In 71% of the studies used the Job Content Questionnaire (from 2 to 49 items) and 19% of the total, the Swedish version (Demand-Control Questionnaire Swedish). Quadrants of the demand-control exposure were used in 51% of the work, but with different cutoff points; scores of the two dimensions were analyzed separately in 27%, and its ratio in 14% of the total. Social support at work was assessed in 44% of the studies. CONCLUSION: Karasek's model should continue to raise epidemiological studies and we hope that researchers face these theoretical and methodological issues outstanding.
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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 | 0.082 | 0.191 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".