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Record W4283386693 · doi:10.3390/rel13070584

Supporting Spirits in Changing Circumstances: Pandemic Lessons for Long-Term Care and Retirement Homes

2022· article· en· W4283386693 on OpenAlexafffundabout
Jane Kuepfer

Bibliographic record

VenueReligions · 2022
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsResearch Institute for AgingUniversity of Waterloo
FundersResearch Institute for Aging, University of WaterlooUniversity of Waterloo
KeywordsActive listeningPandemicGriefValue (mathematics)Public relationsPsychologyLong-term careVariety (cybernetics)Health careNursingFocus groupBusinessCoronavirus disease 2019 (COVID-19)MedicineMarketingPolitical scienceDisease

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.401
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes3
Has abstractyes

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