Achieving Resilience in Primary Care during the COVID-19 Pandemic: Competing Visions and Lessons from Alberta
Bibliographic record
Abstract
The COVID-19 pandemic has tested the resilience of health systems broadly and primary care (PC) specifically. This paper begins by distinguishing the technical and political aspects of resilience and then draws on a documentary analysis and qualitative interviews with health system and PC stakeholders to examine competing resilience-focused responses to the pandemic in Alberta, Canada. We describe the pre-existing linkages between the province's central service delivery agency and its independent PC clinics. Together, these central and independent elements make up Alberta's broader health system, with the focus of this paper being on PC's particular vision of how resilience ought to be achieved. We describe two specific, pandemic-affected areas of activity by showing how competing visions of resilience emerged in the central service delivery agency and independent PC responses as they met at the system's points of linkage. At the first point of linkage, we describe the centralized activation of an incident management system and the replies made by independent PC stakeholders. At the second point of linkage, we describe central efforts to disseminate infection prevention and control guidance to PC clinics and the improvisational efforts of staff at those independent clinics to operationalize the guidance and ensure continuity of operations. We identify gaps between the resilience visions of the central agency and independent PC, drawing broadly applicable policy lessons for improving responses in present and future public health emergencies. Finding ways to include PC in centralized resilience policy planning is a priority.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".