Recommendations for Public Health Workforce Training and Practice Competencies for Digital Technologies in Public Health: A rapid review and synthesis
Bibliographic record
Abstract
This study represents the first step in informing discourse on upgrades to public health training curricula both for graduate school prepared practitioners and for those undergoing continuing education, especially in Canada. We aim to conduct a rapid review to identify recommendations for training and practice competencies to enhance public health practitioners’ capacity to integrate digital technologies in public health functions. We also aim to identify disciplinary perspectives and approaches that can facilitate identified training competencies.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Rapid review of training and practice competencies for digital technologies in the public health workforce; the object is professional practitioner training and curricula rather than the research workforce or research practice.
The review addresses public health workforce training competencies rather than evidence synthesis or research practice.
Public health practice workforce digital competencies, not research workforce or metaresearch methods.
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 | 0.153 | 0.336 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".