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Record W2048112809 · doi:10.1111/hsc.12120

Client safety in assisted living: perspectives from clients, personal support workers and administrative staff in Toronto, Canada

2014· article· en· W2048112809 on OpenAlexaffabout
Brittany Speller, Paul Stolee

Bibliographic record

VenueHealth & Social Care in the Community · 2014
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNursingQualitative researchResidenceContent analysisPopulationIndependent livingMedicinePsychologyGerontologySociologyEnvironmental health

Abstract

fetched live from OpenAlex

As the population ages, the demand for long-term care settings is expected to increase. Assisted living is a suitable and favourable residence for older individuals to receive care services specific to their needs while maintaining their independence and privacy. With the growing transition of older individuals into assisted living, facilities need to ensure that safe care is continually maintained. The purpose of this study was to determine the gaps and strengths in care related to safety in assisted living facilities (ALFs). A qualitative descriptive research design was used to provide a comprehensive understanding of client safety from the perspectives of clients, administrative staff and personal support workers. Interviews were conducted with 22 key informants from three ALFs in Toronto, Ontario throughout July 2012. All interviews were semi-structured, audio-recorded and transcribed verbatim. Initial deductive analysis used directed coding based on a prior literature review, followed by inductive analysis to determine themes. Three themes emerged relating to the safety of clients in ALFs: meaning of safety, a multi-faceted approach to providing safe care and perceived areas of improvement. Sub-themes also emerged including physical safety, multiple factors, working as a team, respecting clients' independence, communication and increased education and available resources. The study findings can contribute to the improvement and development of new processes to maintain and continually ensure safe care in ALFs.

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 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)0.003
metaresearch head score (Gemma)0.007
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 score0.066
Threshold uncertainty score0.399

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0230.005
Scholarly communication0.0050.001
Open science0.0020.004
Research integrity0.0010.002
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 head0.063
GPT teacher head0.418
Teacher spread0.355 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations8
Published2014
Admission routes2
Has abstractyes

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