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Record W4365516020 · doi:10.1186/s12912-023-01289-7

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

2023· article· en· W4365516020 on OpenAlexafffund
Emily Dymchuk, Bita Mirhashemi, Stephanie Chamberlain, Anna Beeber, Matthias Hoben

Bibliographic record

VenueBMC Nursing · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsYork UniversityUniversity of Alberta
FundersAlzheimer Society
KeywordsPreparednessNursingMedicineFamily caregiversPandemicFocus groupQualitative researchPublic healthHealth carePerspective (graphical)Long-term careCaregiver stressCoronavirus disease 2019 (COVID-19)Disease

Abstract

fetched live from OpenAlex

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0090.008
Scholarly communication0.0040.004
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.174
GPT teacher head0.478
Teacher spread0.304 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations10
Published2023
Admission routes2
Has abstractyes

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