Assessing self-reported core competencies of public health practitioners in Lebanon using the WHO-ASPHER validated scale: a pilot study
Bibliographic record
Abstract
BACKGROUND: The World Health Organization and the Association of Schools of Public Health in the European Region recommend the self-assessment of public health core competencies to strengthen the proficiency of the public health workforce and prepare them for future challenges. A framework for these competencies is lacking and highly needed in Lebanon. This study aims to validate the WHO-ASPHER self-declared scale and evaluate the perceived competency level of the different categories of Lebanese public health practitioners. METHODS: This population-based cross-sectional study conducted online between July and September 2021 involved 66 public health practitioners who graduated from different universities in Lebanon. Data were collected using the snowball technique via a self-report questionnaire that assessed public health proficiency, categorized into 1) content and context, 2) relationship and interactions, and 3) performance and achievements. The rotated component matrix technique was used to test the construct validity of the scales. Bivariate and multivariate analyses were performed after ensuring the adequacy of the models. Significance was set at a p-value < 0.05. RESULTS: The factor analysis for scale domains showed that the Barlett test sphericity was significant (p < 0.001), high loadings of items on factors, and Cronbach's alpha values of more than 0.9 in all three categories, showing an appropriate scale validity and reliability. The perceived level of competencies was significantly different between public health professionals and other health professionals with public health activities. All respondents scored low in most public health categories, mainly science and practice. CONCLUSION: Data findings showed variability of self-declared gaps in knowledge and proficiency, suggesting the need to review the national public health education programs. Our study offers a valuable tool for academia and public health professionals to self-assess the level of public health proficiency and guide continuous education needs for professional development.
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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 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".