Psychometric analysis and construct validation of Health Professional Education in Patient Safety Survey in the Indian context
Bibliographic record
Abstract
Background Improving patient safety (PS) is critical to optimizing healthcare delivery. There is a need to develop curricula or incorporate PS concepts in health professionals' (HPs) education, in both theoretical and practical training. Consequently, there is a need to measure the perception of HPs regarding various PS competencies imparted to them during their training. The Health Professional Education in Patient Safety Survey (H-PEPSS) is a tool that measures HPs' self-reported PS competence and was designed to reflect six sociocultural areas central to PS. The tool has been validated in Canada but not in India. We did a confirmatory factor analysis (CFA) and psychometric validation of the H-PEPSS in the Indian context. Methods The sample comprised 240 HPs. We used the maximum likelihood estimation method on AMOS V20 (SPSS Inc.) to carry out a CFA of the tool. We used the normed fit index, Tucker-Lewis index, comparative fit index, standard root mean square residual, root mean square residual and root mean square error of approximation to evaluate the model fit. Internal consistency and reliability of the six factors of the model were examined using Cronbach's alpha. Convergent validity of the model was examined using average variance extracted and composite reliability. Discriminant validity was examined using the Fornell and Larcker criterion and the heterotrait-monotrait method. Results The results indicate a good fit. The H-PEPSS was found to be reliable and valid for assessing PS competencies among HPs. Comparison of the results with the results of the Canadian setting confirmed external validity. Conclusion The 16-item H-PEPSS has good psychometric properties for use in the Indian context. The 23-item HPEPSS was found to be reliable and valid for assessing PS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".