Early Impacts of the COVID-19 Pandemic on Public Health Systems and Practice in 3 Canadian Provinces From the Perspective of Public Health Leaders: A Qualitative Study
Bibliographic record
Abstract
CONTEXT: The COVID-19 pandemic has impacted health systems worldwide. Studies to date have largely focused on the health care system with less attention to the impact on public health systems and practice. OBJECTIVE: To describe the early impacts of COVID-19 on public health systems and practice in 3 Canadian provinces from the perspective of public health system leaders and synthesize lessons learned. DESIGN: A qualitative study using semistructured virtual interviews with public health leaders between October 2020 and April 2021. The World Health Organization's essential public health operations framework guided data collection and analysis. SETTING: This study involved the Canadian provinces of Alberta, Ontario, and Québec. These provinces were chosen for their large populations, relatively high COVID-19 burden, and variation in public health systems. PARTICIPANTS: Public health leaders from Alberta (n = 21), Ontario (n = 18), and Québec (n = 19) in organizations with a primary mandate of stewardship and/or administration of essential public health operations (total n = 58). RESULTS: We found that the COVID-19 pandemic led to intensified collaboration in public health systems and a change in workforce capacity to respond to the pandemic. This came with opportunities but also challenges of burnout and disruption of non-COVID-19 services. Information systems and digital technologies were increasingly used and there was greater proximity between public health leaders and other health system leaders. A renewed recognition for public health work was also highlighted. CONCLUSIONS: The COVID-19 pandemic impacted several aspects of public health systems in the provinces studied. Our findings can help public health leaders and policy makers identify areas for further investment (eg, intersectoral collaboration, information systems) and develop plans to address challenges (eg, disrupted services, workforce burnout) that have surfaced.
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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 | 0.132 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".