Deployed in Disaster: Exploratory Study of Personnel Deployed into Ontario Long-Term Care Homes during the COVID-19 Pandemic
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic had a devastating impact on long-term care in Canada, exacerbating an existing crisis of staff shortages, inadequate infrastructure and funding, into a disaster. In response, the province of Ontario enacted emergency legislation and requested federal government support, resulting in the deployment of personnel from the Canadian Armed Forces and acute care hospitals into long-term care homes across the province. This exploratory study aims to develop a rich description of the long-term care context during the pandemic, deployed personnel's perspectives on providing care in the context, and identification of lessons learned while working during the pandemic. Method: Descriptive exploratory design with demographic questionnaire and semi-structured interviews will be used to understand the background and perspective of deployed personnel and managers on working in long-term care during the pandemic. Thematic analysis will be used to analyze the transcripts, organize codes, and identify and describe major themes. Findings will also be compared with disaster literature to understand how the perspectives of deployed personnel compare with existing disaster research. Results: 21 interviews were initially conducted. Analysis of these interviews identified key challenges experienced by those deployed, including human resources, leadership and accountability, and policies and regulations. Perspectives and strategies for overcoming these challenges were also shared. Conclusion: The scale, duration, and context of the redeployment of personnel into long-term is unprecedented and has seen little research. This exploratory study shares the experiences of personnel who deployed into long-term care and helps identify lessons learned from overcoming challenges in the disaster context. These findings will be able to inform future disaster research and how to better prepare responders in the future.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".