The Employment Precariousness Scale (EPRES): psychometric properties of a new tool for epidemiological studies among waged and salaried workers
Bibliographic record
Abstract
BACKGROUND: Despite the fact that labour market flexibility has resulted in an expansion of precarious employment in industrialised countries, to date there is limited empirical evidence concerning its health consequences. The Employment Precariousness Scale (EPRES) is a newly developed, theory-based, multidimensional questionnaire specifically devised for epidemiological studies among waged and salaried workers. OBJECTIVE: To assess the acceptability, reliability and construct validity of EPRES in a sample of waged and salaried workers in Spain. METHODS: A sample of 6968 temporary and permanent workers from a population-based survey carried out in 2004-2005 was analysed. The survey questionnaire was interviewer administered and included the six EPRES subscales, and measures of the psychosocial work environment (COPSOQ ISTAS21) and perceived general and mental health (SF-36). RESULTS: A high response rate to all EPRES items indicated good acceptability; Cronbach's alpha coefficients, over 0.70 for all subscales and the global score, demonstrated good internal consistency reliability; exploratory factor analysis using principal axis analysis and varimax rotation confirmed the six-subscale structure and the theoretical allocation of all items. Patterns across known groups and correlation coefficients with psychosocial work environment measures and perceived health demonstrated the expected relations, providing evidence of construct validity. CONCLUSIONS: Our results provide evidence in support of the psychometric properties of EPRES, which appears to be a promising tool for the measurement of employment precariousness in public health research.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".