Public sector health analytics capacity before and after Covid-19: A case study of manager perspectives in New Brunswick, Canada
Bibliographic record
Abstract
Background: Demand for health data and analytics to support research, policy, and practice continues to rise, accelerated by the Covid-19 pandemic. Despite the importance of the government analytics workforce in driving academic-based data sharing and linkage platforms, little is known about how public sector managers assess capacity in health analytics. This case study describes findings from consultations among middle managers of analytics services in a Canadian provincial health ministry. Methods: Data collection involved a mixed-questions survey to gauge the functional perspective of managers on organisational and human resource analytics capacity within the New Brunswick Department of Health. The repeated cross-sectional survey was implemented in two rounds, with a baseline collected before the Covid-19 global outbreak (in 2016) and a follow-up after the pandemic emergency response (in 2022). Results: The post-pandemic period was associated with perceptions of a growing role for public service personnel in handling analytics. Recruitment and retention of skilled analytics professionals emerged as the top priority for capacity building, including needs-based planning, competitive compensation packages to address skills shortages, professional development and promotion opportunities, and tracking key performance indicators for employee satisfaction. Conclusions: Government health analytics professionals play a critical role in advancing administrative data use and re-use. Enhanced knowledge sharing is needed on best practices in supply-demand monitoring for analytics professionals and planning for human resources surge capacity in the public service, lest significant innovation potential for health system improvement be left untapped.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".