Prioritizing professional competencies in environmental public health: A best–worst scaling experiment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 0.002 |
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 it