Considerations for adapting digital competencies and training approaches to the public health workforce: an interpretive description of practitioners’ perspectives in Canada
Bibliographic record
Abstract
BACKGROUND: Widespread digital transformation necessitates developing digital competencies for public health practice. Given work in 2024 to update Canada's public health core competencies, there are opportunities to consider digital competencies. In our previous research, we identified digital competency and training recommendations within the literature. In this study, we explored public health practitioners' experiences and perspectives on adapting identified digital competencies and training recommendations for Canada. METHODS: Between November and December 2023, we conducted an interpretive description using four focus groups with 19 public health practitioners working in regional and federal health authorities across Canada, with at least 3 years' experience in current roles and experience using digital technologies in practice. We explored practitioners' experiences using digital technologies and sought their opinions on how digital competency recommendations previously identified could be adapted to Canada's context. To generate deep insights of practitioners' subjective experiences and perspectives, we analyzed verbatim transcripts using Braun and Clarke's reflexive thematic analysis. RESULTS: We identified three main themes: a) public health systems must evolve to support new digital competencies; b) strengthen the basics before extending towards digital competencies; and c) focus on building general digital competencies with options for specialization where necessary. Findings emphasized matching workforce digital competencies to public health system capabilities and meaningfully integrating digital competencies within existing curricula. Such integration can consider how digital technologies change current public health practice to ensure practitioners are better able to address contemporary public health problems. Findings demonstrated roles for specialized digital programs as resources for learning within health systems and emphasized hands-on real-world training approaches. CONCLUSION: We need integrated, systems-focused approaches to digital competencies cutting across the current public health curriculum, while creating space for specialized digital public health competencies and roles. Further research is needed to understand requirements for enacting these recommendations in practice.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".