Adaptación transcultural y validación del cuestionario de cultura preventiva Organizational Performance Metric
Bibliographic record
Abstract
OBJECTIVE: Safety positive performance indicators (PPI) reflect key aspects of safety culture; some of them also have predictive validity for occupational injuries. This is the case of the Organizational Performance Metric (OPM), developed by the Canadian Institute for Work & Health (IWH), a short, widely validated international English language questionnaire. The objective of this study was to obtain a transculturally adapted Spanish language version ofthe OPM, and to analyze its statistical reliability, validity and internal consistency. METHOD: After a translation and back translation process was performed by an expert panel, 478 questionnaires were completed in Navarra, Spain. We calculated the Cronbach alpha coefficient, bivariate correlations and the intra-class correlation coefficient (ICC) and performed exploratory factorial analysis of all eight items. RESULTS: Data show the new tool has high reliability (Cronbach alpha==0.863) and internal validity (ICC=0.842). The factorial analysis confirmed a single latent factor among the eight items of the questionnaire. CONCLUSIONS: the adapted questionnaire (OPM-Esp) constitutes a valid instrument for use as an indicator of safety performance in Spanish companies. Its brevity and simplicity make it especially useful in the work environment. Its ability to predict occupational injuries should be tested in the Spanish context.
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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 | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".