Prioritizing professional competencies in environmental public health: A best–worst scaling experiment
Bibliographic record
Abstract
The professional development of environmental public health professionals in Canada is guided by a set of 133 discipline-specific competencies. Given the diversity of practice in environmental public health, certain competencies may be more important to job effectiveness depending on a practitioner’s context. However, the most important competencies to job effectiveness by context are unknown. Thus, the objectives of this study were to prioritize the discipline-specific competencies according to their importance to job effectiveness, and determine if importance varied by demographic variables. A quantitative discrete-choice method termed best–worst scaling was used to determine the relative importance of competencies. Discrete choice information was electronically collected and analyzed using Hierarchical Bayesian analysis. Our analysis indicates that communication was most important to job effectiveness relative to the other categories. Competency statements within each category differed in their importance to job effectiveness. Further, management and front-line practitioners differed in the importance placed on five of the eight categories. This information can be used to guide new training opportunities, thereby investing in the capacity of environmental health professionals to better protect population health.
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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 | 0.040 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".