The Role of Leaders in Shifting to Virtual Primary Care Delivery during the COVID-19 Pandemic in Ontario’s CHCs
Bibliographic record
Abstract
Context: The emergence of the SARS-Cov-2 virus presented a severe and unprecedented crisis for primary care organizations, which required senior and mid-level leaders to facilitate a rapid transition from in-person to virtual care delivery. Leaders can intensify, attenuate, or reframe and leverage the negative outcomes of extreme exogenous events such as this pandemic - depending on the leadership behaviours they exhibit. Understanding how leaders can best support primary care providers during crises can help governments and organizations better prepare for future events. Objective: To examine the role of leaders in managing the organizational crisis spurred by the pandemic. Study Design and Analysis: The COVID-19 Leadership Survey was adapted for the Canadian context and validated using exploratory factor analysis. Regression analyses were conducted to explore the relationship between leadership behaviours during the initial months of the pandemic and staff’s (a) commitment to change (i.e., to virtual service delivery) and (b) perceived individual/team performance. Setting: Community Health Centres (CHCs) in Ottawa, Canada. Population Studied: Leaders and staff (care providers) overseeing or providing virtual care during the pandemic. Intervention/Instrument: COVID-19 Leadership Survey. Outcome Measures: Care provider commitment to change and perceived individual/team performance survey scales. Results: Nineteen leaders and 78 staff from four purposefully selected CHCs responded. Exploratory factor analysis showed four scales describing leadership behaviours: empowerment, communication about work, communication about change, and teamwork. Leadership behaviours that were team-focused related to perceived individual performance among staff. Staff commitment to change was largely not influenced by leadership behaviours and may be explained by other factors we did not measure, such as those related to workload and work environment. Conclusions: The COVID-19 Leadership Survey may be adapted for use by others to build evidence on crisis leadership and help inform leadership practice. Our results suggest that during crises, leaders should prioritize teamwork by continually ensuring members function as a “real team” even as tasks and team membership evolve.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".