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The impact of COVID-19 on relationships between family/friend caregivers and care staff in continuing care facilities: a qualitative descriptive analysis

2024· other· en· W6977823148 on OpenAlexaff

Bibliographic record

VenueFigshare · 2024
Typeother
Languageen
FieldSocial Sciences
TopicHealth, Education, and Cultural Studies
Canadian institutionsYork UniversityUniversity of Alberta
Fundersnot available
KeywordsPreparednessPandemicFocus groupQualitative researchHealth carePerspective (graphical)Public healthDescriptive research

Abstract

fetched live from OpenAlex

Abstract Background The COVID-19 pandemic and related public health measures added a new dynamic to the relationship between caregivers and care staff in congregate care settings. While both caregivers and staff play an important role in resident quality of life and care, it is common for conflict to exist between them. These issues were amplified by pandemic restrictions, impacting not only caregivers and care staff, but also residents. While research has explored the relationship between caregivers and care staff in long-term care and assisted living homes, much of the research has focused on the caregiver perspective. Our objective was to explore the impact of COVID-19-related public health measures on caregiver-staff relationships from the perspective of staff in long-term care and assisted living homes. Methods We conducted 9 focus groups and 2 semi-structured interviews via videoconference. Results We identified four themes related to caregiver-staff relationships: (1) pressure from caregivers, (2) caregiver-staff conflict, (3) support from caregivers, and (4) staff supporting caregivers. Conclusions The COVID-19 pandemic disrupted long-standing relationships between caregivers and care staff, negatively impacting care staff, caregivers, and residents. However, staff also reported encouraging examples of successful collaboration and support from caregivers. Learning from these promising practices will be critical to improving preparedness for future public health crises, as well as quality of resident care and life in general.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.157
GPT teacher head0.429
Teacher spread0.272 · 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 designQualitative
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

Citations0
Published2024
Admission routes1
Has abstractyes

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