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The nature of safety problems among Canadian homecare clients: evidence from the RAI-HC<sup>©</sup>reporting system

2009· article· en· W2098608721 on OpenAlexafffundabout
Diane Doran, John P. Hirdes, Régis Blais, G. Ross Baker, Jennie Pickard, Micaela Jantzi

Bibliographic record

VenueJournal of Nursing Management · 2009
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversité de MontréalHomewood Research InstituteSante MontrealUniversity of WaterlooUniversity of Toronto
FundersHealth CanadaCanadian Patient Safety Institute
KeywordsNova scotiaPatient safetyMedicineFamily medicineIncident reportHealth careNursingMedical emergencyGeography

Abstract

fetched live from OpenAlex

AIM(S): The purpose of this study was to identify the nature of patient safety problems among Canadian homecare (HC) clients, using data collected through the RAI-HC((c)) assessment instrument. BACKGROUND: Problems of patient safety have been well documented in hospitals. However, we have very limited data about patient safety problems among HC clients. METHOD(S): The study methodology involved a secondary analysis of data collected through the Canadian home care reporting system. The study sample consisted of all HC clients who qualified to receive a RAI-HC assessment from Ontario, Nova Scotia and Winnipeg Regional Health Authority for the 2003-2007 reporting period. There were a total of 238 958 cases available for analysis; 205 953 from Ontario, 26 751 from Nova Scotia and 6254 from Winnipeg Regional Health Authority. RESULTS: New fall (11%), unintended weight loss (9%), new emergency room (ER) visits (7%) and new hospital visits (8%) were the most prevalent potential adverse events identified in our study. A small proportion of the HC clients experienced a new urinary tract infection (2%). CONCLUSION(S): Understanding clients' risk profiles is foundational to effective patient care management. IMPLICATIONS FOR NURSING MANAGEMENT: We need to begin to develop evidence about best practices for ameliorating safety risk.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.040
GPT teacher head0.364
Teacher spread0.324 · 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 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
Published2009
Admission routes3
Has abstractyes

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