Health-care providers' experiences during the COVID-19 pandemic: lessons for leaders
Bibliographic record
Abstract
PURPOSE: The purpose of this qualitative research study is to explore health-care providers' perspectives and experiences with a specific focus on supports reported to be effective during the COVID-19 pandemic. The overarching goal of this study is to inform leaders and leadership regarding provision of supports that could be implemented during times of crisis and in the future beyond the pandemic. DESIGN/METHODOLOGY/APPROACH: Data were collected by semi-structured, conversational interviews with a sample of 33 health-care professionals, including Registered Nurses, Nurse Practitioners, Registered Psychologists, Registered Dieticians and an Occupational Therapist. FINDINGS: Three major themes emerged from the interview data: (1) professional and personal challenges for health-care providers, (2) physical and mental health impacts on health-care providers and (3) providing supports for health-care providers. The third theme was further delineated into three sub-theses: formal resources and supports, informal resources and supports and leadership strategies. ORIGINALITY/VALUE: Health-care leaders are advised to pay attention to the voices of the people they are leading. It is important to know what supports health-care providers need in times of crisis. Situating the needs of health-care providers in the Carter and Bogue Model of Leadership Influence for Health Professional Wellbeing (2022) can assist leaders to deliberately focus on aspects of providers' wellbeing and remain cognizant of the supports needed both during a crisis and when circumstances are unremarkable.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".