Identifying core competencies for public health epidemiologists.
Bibliographic record
Abstract
BACKGROUND: Public health authorities have prioritized the identification of competencies, yet little empirical data exist to support decisions on competency selection among particular disciplines. We sought perspectives on important competencies among epidemiologists familiar with or practicing in public health settings (local to national). METHODS: Using a sequential, qualitative-quantitative mixed method design, we conducted key informant interviews with 12 public health practitioners familiar with front-line epidemiologists' practice, followed by a web-based survey of members of a provincial association of public health epidemiologists (90 respondents of 155 eligible) and a consensus workshop. Competency statements were drawn from existing core competency lists and those identified by key informants, and ranked by extent of agreement in importance for entry-level practitioners. RESULTS: Competencies in quantitative methods and analysis, critical appraisal of scientific evidence and knowledge transfer of scientific data to other members of the public health team were all regarded as very important for public health epidemiologists. Epidemiologist competencies focused on the provision, interpretation and 'translation' of evidence to inform decision-making by other public health professionals. Considerable tension existed around some potential competency items, particularly in the areas of more advanced database and data-analytic skills. INTERPRETATION: Empirical data can inform discussions of discipline-specific competencies as one input to decisions about competencies appropriate for epidemiologists in the public health workforce.
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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 | 0.038 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".