Building the Workforce’s Capacity to Support the Digital Transformation of Public Health: Environmental Scan of Training Programs for Digital Technologies in Public Health
Bibliographic record
Abstract
Background: The digital transformation of society and public health has created an urgent need for new competencies to address evolving and contemporary public health challenges. While some public health institutions and schools worldwide have begun responding through various training programs and approaches, many have yet to do so. A clearer understanding of the current training landscape can inform more coordinated efforts to update curricula and strengthen digital competency within the public health workforce. Objective: This study aimed to map and describe existing digital public health (DPH) training programs, identifying common curricula content, disciplinary involvement, and training approaches. It also aimed to identify gaps and opportunities for curricular adaptation. Methods: This environmental scan was conducted in 2 stages, drawing on guidance from studies by Rowel et al and Wilburn et al. First, we performed a systematic search of DPH training programs, followed by interviews with selected program directors to explore their program design and implementation. The scan emphasized a transdisciplinary lens, consistent with the evolving nature of DPH. Between March and May 2023, we searched 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 4 directors of identified programs exploring program characteristics and the inter- or transdisciplinary partnerships essential to their design. Search data were summarized using narrative synthesis, while content analysis was applied to the interview data. Results: Overall, 58 DPH training programs were identified, categorized into 3 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 revealed that motivation for developing interdisciplinary DPH programs stemmed from the need to meet evolving job market demands and respond to calls for curricular renewal among professional bodies. Effective design and delivery were supported by academic-industry partnerships, which aimed to cultivate professionals with depth in public health and breadth in digital competencies. These programs drew on diverse disciplinary perspectives from academia, the public sector, and private industry. However, sustaining such partnerships was challenged by the need to negotiate shared priorities, reconcile differing viewpoints, and secure ongoing funding. 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 existing curricula, offering stand-alone 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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".