Leaving Long-Term Care during a Global Pandemic: Decision-Making and Experiences of Residents, Care Partners, and Health Professionals in Ontario
Bibliographic record
Abstract
The COVID-19 pandemic exposed pre-existing vulnerabilities in long-term care (LTC) sectors globally, prompting a change in the decision-making and care trajectories of many residents. In Ontario, some care partners responded to the crisis in LTC by moving residents home with them for the duration or a portion of the pandemic. This type of care transition, from LTC to home care, was previously uncommon and complex to arrange due to the multiple stakeholders involved. This thesis aims to explore the nature of individual and group decision-making during periods of crisis and change. The research comprises a scoping review and an exploratory qualitative study. Findings are presented across three interconnected papers. Paper 1 is a scoping review that synthesizes existing knowledge on institutional care transition planning from the perspectives of the care triad (i.e., older adults, care partners and health professionals). 39 articles were included and stakeholders’ perspectives on, and involvement in, care transition planning is discussed. Seven factors appeared to guide decision-making: (a) institutional priorities and requirements; (b) resources; (c) knowledge; (d) risk; (e) group structure and dynamic; (f) health and support needs; and (g) personality, preferences and beliefs. Papers 2 and 3 present a qualitative description of the care triad’s transition decision-making in Ontario’s LTC settings during the COVID-19 pandemic. Interviews were conducted with 3 residents, 18 care partners, and 11 health professionals. Paper 2 discusses stakeholder involvement in care transition planning during the pandemic and the factors that guided their decision-making. Decisions to move a resident out of LTC during the pandemic were largely made by care partners, and stakeholders considered each of the seven factors identified in Paper 1. Paper 3 analyzes stakeholders’ decision-making through the lens of Crisis Decision Theory. Individual and group differences in how participants assessed the severity of the crisis and evaluated response options were identified. Overall, this thesis demonstrates the complexity of care transition planning from multiple perspectives. The findings provide insights towards individual and group decision-making in crisis situations, and can be used to better support Ontarians through LTC to community transitions, particularly in event of future pandemics.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".