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Record W2997947329 · doi:10.1097/nur.0000000000000281

Development and Pilot Implementation of a Search Protocol to Improve Patient Safety on a Psychiatric Inpatient Unit

2017· article· en· W2997947329 on OpenAlexaffabout
Frances Abela-Dimech, Kim Johnston, Gillian Strudwick

Bibliographic record

VenueClinical Nurse Specialist · 2017
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsProtocol (science)Unit (ring theory)Patient safetyMnemonicMedicineMedical emergencyNursingPsychologyHealth careAlternative medicine

Abstract

fetched live from OpenAlex

PURPOSE/OBJECTIVE: A mental health organization in Ontario, Canada, noted an increase in unsafe items entering locked inpatient units. The purpose of this project was to develop and implement a search protocol to improve patient, staff, and visitor safety by preventing unsafe items from entering a locked inpatient unit. DESCRIPTION OF THE PROJECT: Under the guidance of a clinical nurse specialist, an interprofessional team used the Failure Mode and Effects Analysis framework to identify what items were considered unsafe, how these unsafe items were entering the unit, and what strategies could be used to prevent these items from entering the unit. A standardized search protocol was identified as a strategy to prevent items from entering the unit. OUTCOME: The standardized search protocol was developed and piloted on 1 unit. To support the search protocol, an interprofessional team created a poster using a mnemonic aid to educate patients, staff, and visitors about which items could not be brought onto the unit. Educational sessions on the search protocol were provided for staff. The difference between the number of incidents before and after the implementation of the search protocol was statistically significant. CONCLUSIONS: Safety on an inpatient unit was increased as incidents of unsafe items entering the unit decreased.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score0.976

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.214
GPT teacher head0.571
Teacher spread0.356 · 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

Citations22
Published2017
Admission routes2
Has abstractyes

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