Informing organizational strategies for digital public health: A qualitative description of practitioners’ perspectives on opportunities and challenges in a provincial public health organization in Canada (Preprint)
Bibliographic record
Abstract
BACKGROUND The digital transformation of health services accelerated during the pandemic. While “digital health” strategies were created, they paid minimal attention to public health services like health promotion, disease surveillance, emergency preparedness, and health protection. OBJECTIVE To inform a digital public health (DPH) strategy at the BC Centre for Disease Control (BCCDC), we explored public health practitioners’ perspectives on challenges and opportunities of integrating digital technologies into public health functions within the organization. METHODS In this qualitative description, we conducted 18 focus groups (FGs) between January and June 2023, drawing practitioners from nine organizational subunits of the BCCDC including population and public health, environmental health, clinical services, vaccine preventable diseases, communications, knowledge translation, data analytics and Indigenous health (2 FGs per subunit). Discussions explored practitioners’ application of digital technologies in their public health work, focusing on challenges encountered during implementation (current state FGs) and perceived opportunities (future state FGs). Sessions were audio-recorded, and detailed field notes were taken. Thematic analysis was conducted, comparing perspectives across groups using constant comparative techniques. RESULTS We identified three themes. First, “bridging existing inequities - an opportunity and a challenge contingent on public trust” described participants’ excitement about opportunities for DPH to disrupt historical inequities if centred on trust and reconciliation, while recognizing current digital transformation efforts risk exacerbating existing inequities with the digital divide. Second “a sense of disconnect between “digital” and “public health” functions” described perceptions of DPH as being out of scope of core public health duties, requiring new competencies and navigation of complex organizational policies for which support is suboptimal. Third, “balancing the need for responsive DPH with necessary reactivity” highlighted practitioners’ yearnings for a proactive DPH strategy rather than current issue-based reactive approaches. Participants suggest a centralized systematic program can help achieve this goal. CONCLUSIONS A cohesive, systematic, and proactive organizational strategy for DPH is critical to enable equity-focused digital transformation. Such a strategy can bridge perceived disconnects between digital and public health functions through organizational supports like competency development and streamlined policies can better support public health practitioners to integrate digital technologies into their work. CLINICALTRIAL N/A
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.018 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".