Supporting Spirits in Changing Circumstances: Pandemic Lessons for Long-Term Care and Retirement Homes
Bibliographic record
Abstract
The pandemic of 2020–2022 brought both disruption to, and increased need for, spiritual care in long-term care and retirement homes. This paper reports and reflects upon the experiences of spiritual care providers (SCPs) in these settings in Ontario, Canada as they each endeavored to adapt to their circumstances. Qualitative data were gathered from 27 participants through a variety of means, including natural focus group opportunities, email responses to questions, and in-depth virtual interviews. This study learned that during the pandemic, SCPs creatively adapted the care they provide, while finding it challenging to meet needs for touch, community, mental health care, and processing grief. SCPs spoke with confidence about their role as a listening and supportive presence, for team and family reassurance, as well as for residents. Opportunities to personalize spiritual care using technology, and the value of small, intimate gatherings were realized, along with the value of employing an in-house SCP who truly gets to know residents and can continue to creatively adapt to meet changing needs in changing circumstances. Recommendations are made for spiritual care provision that is resilient through outbreak restrictions into 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".