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Record W2269955146 · doi:10.1093/intqhc/mzw006

Types and patterns of safety concerns in home care: client and family caregiver perspectives

2016· article· en· W2269955146 on OpenAlexafffundabout
Catherine Tong, Joanie Sims‐Gould, Anne Martin-Matthews

Bibliographic record

VenueInternational Journal for Quality in Health Care · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsVancouver Coastal HealthUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsNursingBusinessPsychologyMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Drawing on interviews with home care clients and their family caregivers, we sought to understand how these individuals conceptualize safety in the provision and receipt of home care, how they promote safety in the home space and how their safety concerns differ from those of home support workers. DESIGN: In-depth, semi-structured interviews were conducted with clients and family caregivers. The analysis included topic and analytical coding of participants' verbatim accounts. SETTING: Interviews were completed in British Columbia, Canada. PARTICIPANTS: Totally 82 clients and 55 caregivers participated. RESULTS: Clients and family caregivers identified three types of safety concerns: physical, spatial and interpersonal. These concerns are largely multi-dimensional and intersectional. We present a conceptual model of client and caregiver safety concerns. We also examine the factors that intensify and mitigate safety concerns in the home. CONCLUSIONS: In spite of safety concerns, clients and family caregivers overwhelmingly prefer to receive care in the home setting. Spatial and physical concerns are the most salient. The financial burden of creating a safe care space should not be the client's alone to bear. The conceptualization and promotion of safety in home care must recognize the roles, responsibilities and perspectives of all of the actors involved, including workers, clients and their caregivers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.690

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 studies0.0000.000
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 head0.062
GPT teacher head0.476
Teacher spread0.414 · 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

Citations40
Published2016
Admission routes3
Has abstractyes

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