Surge capacity and practice management challenges of Canadian family physicians during COVID-19: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Planning for surge capacity, that is, the ability of a health service to expand beyond normal capacity and meet an increased demand for clinical care, is an essential component of public health emergency preparedness. During the COVID-19 pandemic, family physicians (FPs) were called upon to provide surge capacity in settings such as hospital units and emergency departments while also maintaining their primary care responsibilities. Most research reports on projection models, hospital settings, or the use of virtual care, with limited focus on the firsthand experiences of FPs in this role. To address this gap, this study examines the experiences of FPs and their roles in supporting surge capacity during the COVID-19 pandemic. METHODS: As part of a mixed methods, multiple case study, we conducted semi-structured interviews with FPs between October 2020 and June 2021 across four Canadian provinces (British Columbia, Ontario, Nova Scotia, Newfoundland and Labrador). During the interviews, FPs were asked about the roles they assumed during the different stages of the pandemic and the factors that impacted their ability to fulfil these roles. Interviews were transcribed verbatim and a thematic analysis approach was employed to identify recurring themes. RESULTS: We interviewed a total of 68 FPs across the four provinces and identified two overarching themes: (1) mechanisms used to create surge capacity by FPs, and (2) key considerations for an organized surge capacity program. During the pandemic, surge capacity was achieved by extending FP working hours, expanding the FP workforce, and redeploying FPs to new roles and settings. The effective implementation of FP surge capacity requires organized communication and coordination mechanisms, policies to clarify scope of practice during redeployment, training and mentorship related to new redeployment roles, FPs holding hospital privileges, and policies that help to preserve primary care capacity. CONCLUSIONS: FPs make critical contributions to surge capacity but require structured support to balance their redeployment roles with their ongoing primary care responsibilities. Ensuring adequate coverage for their practices and employing strong communication and coordination mechanisms are essential for maintaining high-quality care and managing the strain on FPs and the health system during public health emergencies.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".