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Record W4220736446 · doi:10.1111/ppc.13065

Increasing capacity to address the physical health needs of patients in a mental health and addictions hospital

2022· article· en· W4220736446 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePerspectives In Psychiatric Care · 2022
Typearticle
Languageen
FieldNursing
TopicNursing Diagnosis and Documentation
Canadian institutionsUniversity of TorontoNorth York General HospitalCentre for Addiction and Mental HealthWestern University
Fundersnot available
KeywordsMental healthAddictionPhysical healthMental capacityPsychiatryPsychologyNursingMedicine

Abstract

fetched live from OpenAlex

PURPOSE: This paper describes the strategy and outcomes of a quality improvement initiative focused on building the capacity of nurses at a mental health and addictions teaching hospital to provide an improved standard of physical health care. Education was provided via a series of e-learning modules and interactive workshops. To reinforce the education and enhance practice change, improvements were made to electronic documentation templates and organizational standards. Further, the organization provided increased access to physical health equipment, a mobile application to support assessments and a reference card for lanyards. CONCLUSIONS: Nurses identified increased confidence in performing physical assessments, and documentation improved with standards and automated forced functionality in the electronic health record. Ultimately, the organization successfully implemented a multifaceted strategy to improve physical healthcare services for people with mental health and substance use concerns. PRACTICE IMPLICATIONS: Organizational investment can lead to sustainable changes in nursing confidence and increased physical health assessment completion.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.009
GPT teacher head0.308
Teacher spread0.299 · 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