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Record W2999886375 · doi:10.1097/ncq.0000000000000453

Quality Standards of Nursing Care for the Use of Chemical Restraints

2020· review· en· W2999886375 on OpenAlexaff
Catherine Hupé, Caroline Larue, Valerie Gazemar, Catherine Pépin, Damien Contandriopoulos

Bibliographic record

VenueJournal of Nursing Care Quality · 2020
Typereview
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversity of Victoria
Fundersnot available
KeywordsQuality (philosophy)Intervention (counseling)NursingPerspective (graphical)Quality managementHealth careMedicineMEDLINENursing careComputer scienceOperations managementManagement systemEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: The use of chemical restraints (CRs) in health care facilities is a complex intervention that raises questions about its effectiveness and whether it is safe and patient oriented. PURPOSE: This review aims to gather nursing quality standards for the use of CR through an innovative method of knowledge synthesis, the realist review, to support the development of a quality evaluation tool. METHODS: A realist review method was chosen. RESULTS: An operational definition of chemical restraint is proposed, a concept seen as synonymous with the management of behavioral symptoms by pharmacological agents with sedative proprieties. Twenty-eight quality standards were identified and presented in a theoretical model. CONCLUSIONS: These quality standards will allow the evaluation of practices from a nursing perspective for the use of CR in health care settings.

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)0.117
metaresearch head score (Gemma)0.236
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.117
Threshold uncertainty score0.616

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1170.236
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0080.006
Science and technology studies0.0020.003
Scholarly communication0.0060.004
Open science0.0030.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.489
GPT teacher head0.615
Teacher spread0.127 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2020
Admission routes1
Has abstractyes

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