Pandemic planning for primary care in Southwestern Ontario: protocol for a sequential multiple-methods design
Bibliographic record
Abstract
INTRODUCTION: During the COVID-19 pandemic, primary care providers rapidly adapted their practice in response to emerging knowledge of infection risk, resource availability and evolving patient and system needs. Given the previous lack of guidance specific to primary care settings, there is a need to formalise and integrate lessons learnt from these successful adaptations into future pandemic plans. Our project will develop a model for pandemic planning in primary care designed for replication in other Canadian regions and internationally to facilitate the scale and spread of effective primary care pandemic response strategies. METHODS AND ANALYSIS: We will apply a pragmatic approach and a sequential multiple-methods design consisting of three objectives: (1) scoping review, (2) priority setting exercise and (3) plan creation. For objective 1, we will conduct a scoping review to identify the essential supports of a pandemic response plan for primary care at the clinic, community, regional and provincial levels in Canada and comparable countries. We will conduct the review in accordance with the Joanna Briggs Institute methodology (search date July 2025) and synthesise findings based on recurring themes. For objective 2, we will engage primary care providers, public health professionals and health system managers in a priority-setting exercise consisting of deliberative dialogues and a Delphi process. The exercise will involve a presentation and three rounds of online surveys asking participants to rank the importance of various pandemic supports for primary care. We estimate that 100 participants will attend the presentations and 60 will complete all three rounds of the survey. For objective 3, we will co-create clinic-level, community-level and region-level pandemic response plans in Southwestern Ontario through working groups led by primary care providers and public health members of the research team, with an aim to engage approximately 12-24 participants. We will use rapid reviews, stakeholder input and evidence gathered in earlier components of the project to operationalise prioritised supports. ETHICS AND DISSEMINATION: We obtained ethics approval from the Western University Research Ethics Board (project ID: 127132) and will use best practices for promoting consent, confidentiality and data security. We will disseminate findings through regional, national and international conferences, peer-reviewed journal publications and online platforms. Lastly, we will develop and share a toolkit for pandemic planning in primary care to encourage further research and inform future pandemic preparedness efforts.
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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 | 0.129 | 0.098 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.099 | 0.014 |
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".