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Record W4311243601 · doi:10.1080/14927713.2022.2157318

The experiences of recreation staff in Manitoba long-term care homes during the second wave of the COVID-19 pandemic

2022· article· en· W4311243601 on OpenAlexaffvenueabout
Stephanie Chesser, Samantha Steele-Mitchell, Michelle M. Porter

Bibliographic record

VenueLeisure/Loisir · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRecreationPandemicCoronavirus disease 2019 (COVID-19)Long-term careExploratory research2019-20 coronavirus outbreakTerm (time)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Quality (philosophy)Qualitative researchNursingPsychologyPublic relationsMedicineSociologyPolitical scienceVirology

Abstract

fetched live from OpenAlex

Since the outset of the COVID-19 pandemic, long-term care homes within Canada have shifted the ways they operate and deliver vital care and quality of life opportunities for residents. While attention has increasingly been paid to the experiences of some care providers (e.g., nurses, personal support workers) to these pandemic-necessitated changes, decidedly less attention has been devoted to the experiences of those providing recreation opportunities to long-term care residents. This exploratory qualitative study was carried out to help address this knowledge gap. Using in-depth interviews, this study explored how the COVID-19 pandemic has affected six paid recreation staff working within long-term care homes within Manitoba, Canada during the province’s second pandemic wave (late 2020 to early 2021). Our research findings raise important questions about the ways recreation provision within long-term care could be impacted through and after the COVID-19 era.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.264
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.356
Teacher spread0.303 · 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.

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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