Deployed in Disaster: Perspectives of Personnel Deployed in Ontario Long‐Term Care During the Pandemic
Bibliographic record
Abstract
AIM: The aim of the study is to explore the experience and perspectives of healthcare personnel deployed from the hospitals and military into long-term care (LTC) in Ontario during the pandemic. BACKGROUND: Personnel from acute care hospitals, community care, the Canadian Armed Forces (CAF), and the Canadian Red Cross were deployed in support of LTC homes across Ontario. INTRODUCTION: This article reports on the study of personnel deployed into LTC during the COVID-19 pandemic. Understanding the experiences and lessons learned will be useful to improve planning and responses to future pandemics and disasters. METHODS: A descriptive, exploratory research study design with a demographic questionnaire and semi-structured interviews was used. Data analysis used a combination of open and process coding to identify thematic categories. The COREQ checklist was used for reporting on the study. FINDINGS: Interviews were conducted with 30 participants who described common challenges of navigating the unknown, defining one's role, and establishing order. Lessons included the importance of adaptability, flexibility, and pragmatism and emphasized the role of leadership. DISCUSSION: The findings highlight challenges that personnel from civilian and military backgrounds experience when responding to disaster, and underscore the importance of communication, effective leadership, and cohesive teams. These findings are consistent with existing literature, contribute to the understanding of how personnel in disasters address challenges, and inform how to better prepare and train personnel for future disasters. IMPLICATIONS FOR NURSING AND HEALTH POLICY: These findings can be used to inform disaster theory and practice, including disaster competencies such as the International Council of Nurses Disaster Nursing Competencies, and provide a framework to improve disaster planning and responder training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".