Building the workforce’s capacity to support the digital transformation of public health: An environmental scan of training programs for digital technologies in public health (Preprint)
Bibliographic record
Abstract
BACKGROUND The digital transformation of public health highlights the growing need for new digital competencies to tackle evolving and contemporary public health challenges. While some public health institutions and schools worldwide have begun addressing this need through various approaches, many in Canada have yet to do so. To support systematic competency and curriculum development, we mapped and explored existing digital public health (DPH) training programs, identifying common curricula content, approaches and disciplinary perspectives. OBJECTIVE To support systematic competency and curriculum development, we mapped and explored existing digital public health (DPH) training programs, identifying common curricula content, approaches and disciplinary perspectives. METHODS This two-stage environmental scan included a systematic search of DPH training programs and interviews with select program directors, emphasizing a transdisciplinary approach. Between March and May 2023, we conducted a search on Google and public health association directories to identify degree programs and courses (as part of degree awarding programs) focused on building capacity for using digital technologies in public health. We then conducted semi-structured interviews with four directors of identified programs exploring program characteristics and the inter/transdisciplinary partnerships essential to their design. Search data was summarized using narrative synthesis, while content analysis was applied to the interview data. RESULTS Overall, 58 DPH training programs were identified, categorized into three groups: public health data science (29/58, 50%); public health informatics (16/58, 28%); and a mix of programs exploring digital competencies (13/58, 22%) related to project management and addressing the digital determinants of health. Interviews focused on four key categories: (1) Motivation for interdisciplinary DPH programs, highlighting the need to align with current job market demands for practitioners skilled in interdisciplinary practice and addressing pressures for curricular updates from professional bodies; (2) Design and delivery of interdisciplinary programs, emphasizing academic-industry partnerships aimed at developing professionals with depth in public health and breadth in DPH knowledge; (3) Characteristics of inter- and transdisciplinary partnerships, showcasing the involvement of diverse disciplinary perspectives from academia, public, and private sectors in program design and delivery; and (4) Challenges in implementing these partnerships, including difficulties in negotiating shared commitments, reconciling differing perspectives, and securing sustainable funding for such programs. CONCLUSIONS This global scan of DPH training programs found a strong focus on data-centric competencies, with less emphasis on digital skills for health promotion, leadership, and addressing digital determinants of health. Bridging these gaps requires a stepwise approach: integrating digital competencies into curricula, offering standalone programs for specialized skills, and strengthening partnerships to navigate funding and administrative barriers while promoting equity-driven, interdisciplinary collaboration.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".