Crisis leadership behaviors in healthcare: survey validation and influence on staff outcomes in primary care clinics during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic triggered an unprecedented transition from in-person to virtual delivery of primary health care services. Leaders were at the helm of the rapid changes required to make this happen, yet outcomes of leaders’ behaviours were largely unexplored. This study (1) develops and validates the Crisis Leadership and Staff Outcomes (CLSO) Survey and (2) investigates the leadership behaviours exhibited during the transition to virtual care and their influence on select staff outcomes in primary care. Methods We tested the CLSO Survey amongst leaders and staff from four Community Health Centres in Ontario, Canada. The CLSO Survey measures a range of crisis leadership behaviors, such as showing empathy and promoting learning and psychological safety, as well as perceived staff outcomes in four areas: innovation, teamwork, feedback, and commitment to change. We conducted an exploratory factor analysis to investigate factor structure and construct validity. We report on the scale’s internal consistency through Cronbach’s alpha, and associations between leadership scales and staff outcomes through odds ratios. Results There were 78 staff and 21 middle and senior leaders who completed the survey. A 4-factor model emerged, comprised of the leadership behaviors of (1) “task-oriented leadership” and (2) “person-oriented leadership”, and select staff outcomes of (3) “commitment to sustaining change” and (4) “performance self-evaluation”. Scales exhibited strong construct and internal validity. Task- and person-oriented leadership behaviours positively related to the two staff outcomes. Conclusion The CLSO Survey is a reliable measure of leadership behaviours and select staff outcomes. Our results suggest that crisis leadership is multifaceted and both person-oriented and task-oriented leadership behaviours are critical during a crisis to improve perceived staff performance and commitment to change.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".