Multidisciplinary First-Line Healthcare Leaders’ Roles and Experiences During the COVID-19 Pandemic in Ontario Canada
Bibliographic record
Abstract
BACKGROUND: Throughout the COVID-19 pandemic, first-line healthcare leaders across the healthcare system played crucial roles leading, motivating, and supporting staff. PURPOSE: This study aims to describe multidisciplinary first-line healthcare leaders' experiences during the COVID-19 pandemic in Ontario, Canada using transformational and crisis leadership theory. METHODS: A descriptive two-phase (quantitative & qualitative) design was conducted in the spring of 2021. Phase 1 employed an online survey sent via email to first-line leaders from various sectors who were members of healthcare professional associations in Ontario. Participants included nurse managers, professional practice leaders (e.g., occupational and physiotherapists), advanced practice nurses, and clinical educators. In Phase 2, a subset (n = 19) of the Phase 1 participants were interviewed to gain a deeper understanding of these leaders' experiences including role impact and support available. Semistructured individual interviews were conducted and recorded via Zoom©. Inductive and deductive analysis approaches identified key themes. This paper reports the qualitative findings from Phase 2. RESULTS: Leaders' behaviors were representative of the key dimensions of transformational and complexity leadership theories. Recommendations for leading during a crisis included: engaging in self-care activities to manage the personal impact of the crisis; teamwork and collaborative leadership; and support from fellow first-line leaders and senior leaders. Findings can inform healthcare leadership education programs designed to manage future crises for both academic and practice settings. CONCLUSION: Descriptions of first-line healthcare leaders' roles and experiences during multiple waves of the COVID-19 pandemic validated their important contributions within various health sectors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".