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Record W1975511008 · doi:10.12927/hcq.2009.20720

Home Care Safety Perspectives from Clients, Family Members, Caregivers and Paid Providers

2009· article· en· W1975511008 on OpenAlexafffundabout
Ariella Lang, Marilyn Macdonald, Jan Storch, Kari Elliott, Lynn Stevenson, Hélène Lacroix, Susan Donaldson, Serena Corsini‐Munt, Farraminah Francis, Cherie Curry

Bibliographic record

VenueHealthcare Quarterly · 2009
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsVictorian Order of Nurses
FundersCanadian Institutes of Health ResearchSouth East Regional Health AuthorityCanadian Patient Safety Institute
KeywordsNursingPatient safetyService providerBest practiceMedicineMeaning (existential)Service (business)Public relationsHealth carePsychologyBusinessMarketingPolitical science

Abstract

fetched live from OpenAlex

There is a growing demand for home care services in Canada. Yet, overwhelmingly, research on patient safety has focused on institutional settings. The Canadian Patient Safety Institute convened a Core Safety in Home Care Team of researchers and decision-makers to identify priority research areas and to advance patient safety research in home care. As part of this initiative to investigate and extend our understanding of home care safety, key informant interviews were carried out with a wide range of respondents including researchers, decision-makers, service providers and regulators. In-depth audiotaped interviews were conducted in two Canadian provinces. Interpretive descriptive analyses revealed three main themes: the meaning of home care, safety concerns and the place of technology in the future of home care. Given the multidimensionality and complexity of home care as well as the challenges and strains involved, the risk to all the players is becoming increasingly evident.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.020
GPT teacher head0.337
Teacher spread0.317 · 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

Citations49
Published2009
Admission routes3
Has abstractyes

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