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Record W2042906038 · doi:10.1108/09526860310460479

Issues in quality of high‐tech home care: sources of information and staff training in Quebec primary care organizations and relationships with hospitals

2003· article· en· W2042906038 on OpenAlexafffundabout
Pascale Lehoux, Raynald Pineault, L Richard, Jocelyne St-Arnaud, Susan Law, Henk Rosendal

Bibliographic record

VenueInternational Journal of Health Care Quality Assurance · 2003
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsMedicineNursingPsychological interventionQuality (philosophy)BusinessHigh techFamily medicineMedical emergency

Abstract

fetched live from OpenAlex

This study examined the provision of high‐tech home care by Quebec primary care organizations (CLSCs). Four technologies were selected: IV antibiotic therapy, oxygen therapy, parenteral nutrition, and peritoneal dialysis. A postal survey was sent to all CLSCs and a response rate of 69 percent was obtained; 57 percent of CLSCs have been involved in the provision of services related to three of the high‐tech interventions. The most frequently used sources of information are written material provided by manufacturers or by hospitals. CLSCs relied heavily on peer‐to‐peer training and training provided by manufacturers and hospitals. Formal agreements with hospitals regarding patient flow management were established; aspects related to the “content” of care were much less formalized. CLSCs have integrated high‐tech home care to a substantial extent. Our findings raise quality‐of‐care issues. The interface with hospitals needs to be reinforced and emphasis given to the appropriate use of technology.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.016
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.382
Teacher spread0.353 · 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

Citations5
Published2003
Admission routes3
Has abstractyes

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