COVID-19: A Qualitative Analysis of Academic Family Physician Leaders’ Crisis Response
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The onset of the COVID-19 pandemic severely threatened all aspects of academic family medicine, constituting a crisis. Multiple publications have identified recommendations and documented the creative responses of primary care and academic organizations to address these challenges, but there is little research on how decisions came about. Our objective was to gain insight into the context, process, and nature of family medicine leaders' discussions in pivoting to address a crisis. METHODS: We used a qualitative descriptive design to explore new dimensions of existing concepts. The setting was the academic family medicine department at the University of Toronto. To identify leadership themes, we used the constant comparative method to analyze transcripts of monthly meetings of the departmental executive: three meetings immediately before and three following the declaration of a state emergency in Ontario. RESULTS: Six themes were evident before and after the onset of the pandemic: building capacity in academic family medicine; developing leadership; advancing equity, diversity, and inclusion; learner safety and wellness; striving for excellence; and promoting a supportive and collegial environment. Five themes emerged as specific responses to the crisis: situational awareness; increased multidirectional communication; emotional awareness; innovation in education and patient care; and proactive planning for extended adaptation to the pandemic. CONCLUSION: Existing cultural and organizational approaches formed the foundation for the crisis response, while crisis-specific themes reflected skills and attitudes that are essential in clinical family medicine, including adapting to community needs, communication, and emotional awareness.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".