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)
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,012 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».