The experience of primary care teams during the early phase of COVID-19: A qualitative study of primary care practice leaders in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has caused a rapid shift to virtual care in primary care practices around the globe. There has been little focus on the experiences of interprofessional teams through the lens of primary care practice leaders. The objective of this study was to examine the experience of primary care teams during the first wave of the COVID-19 pandemic from the perspective of primary care leadership. METHODS: Qualitative study using qualitative description methods. Executive Directors of interprofessional primary care teams belonging to the Association of Family Health Teams of Ontario (AFHTO) were invited to participate. Executive Directors were interviewed and the interview transcripts were analyzed using thematic analysis. RESULTS: Seventy-one Executive Directors from across all regions of Ontario were interviewed for the study, representing 37% of the AFHTO member clinics. Four themes were identified in the data: i) Complexities of Virtual Care, ii) Continuation of In-person Care, iii) Supporting Patients at Risk, and iv) Stepping up and into New Roles. CONCLUSIONS: Primary care teams rapidly mobilized to deliver the majority of their care virtually, while continuing to provide in-person and home care as required. Major challenges to virtual care included technological infrastructure and unfamiliarity with virtual platforms. Advantages to virtual care included convenience and time savings. Virtual care will likely continue to be an important mode of primary care delivery moving forward.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".