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Record W2481405371 · doi:10.1186/s12913-016-1618-9

Implementation of a clinical practice guideline for schizophrenia in a specialist mental health center: an observational study

2016· article· en· W2481405371 on OpenAlexaff
Ilan Fischler, Sanaz Riahi, Melanie I. Stuckey, Philip E. Klassen

Bibliographic record

VenueBMC Health Services Research · 2016
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsUniversity of TorontoUniversity of Ontario Institute of TechnologyOntario Shores Centre for Mental Health Sciences
Fundersnot available
KeywordsMedicineObservational studyNursing researchHealth informaticsHealth administrationGuidelineMental healthSchizophrenia (object-oriented programming)Public healthCenter (category theory)PsychiatryClinical PracticeFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In mental health settings, implementation of and adherence to clinical practice guidelines (CPGs) is low. Strategies are needed to overcome barriers and facilitate successful implementation of CPGs into standard care. The goals of this study were to develop a framework for the implementation of a CPG for schizophrenia for hospitalized service users in a mental health care facility, and to monitor adherence to the guideline. METHODS: An eight-step framework was developed based on project management principles: 1) the Appraisal Guideline for Research and Evaluation (AGREE) tool was used to rate and select a CPG; 2) an algorithm was created from the guideline; 3) a gap analysis identified clinical services and processes not conforming with the CPG recommendations; 4) a governance structure was created; 5) a modified Delphi process determined key outcome and process adherence metrics; 6) a project charter was developed; 7) clinical informatics ensured that systems and tools were in place to support the CPG; and 8) therapeutic services were realigned to match the requirements of the CPG within specified fiscal constraints. Percent adherence to the identified process adherence metrics was calculated before (March 2014) and for 12 months after implementation (April 2014-March 2015). RESULTS: The National Institute of Health and Care Excellence guideline scored highest on AGREE and was used to develop the algorithm. Cognitive behavior therapy for psychosis (CBT-P), art therapy and carer assessments were identified as gaps in care. Clinical global impression - Schizophrenia score was identified as the primary service user outcome variable and antipsychotic polypharmacy, metabolic monitoring, CBT-P referral and supported employment/vocational services referral as the primary process adherence measures. Adherence to guidance for metabolic monitoring (March 2014, 76.7 %; March 2015, 81.6 %), CBT-P referral (March 2014, 6.5 %; March 2015, 11.4 %) and vocational rehabilitation referral (March 2014, 36.6 %; March 2015, 49.1 %) were increased after CPG implementation. There was an initial increase in adherence to antipsychotic monotherapy (March 2014, 53.4 %; November 2014, 62.7 %), which decreased back toward baseline (March 2015, 55.1 %). CONCLUSIONS: The eight-step framework was used to implement a CPG process, though further quality improvements initiatives may be needed to improve adherence.

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)0.041
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.189
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0410.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.666
GPT teacher head0.712
Teacher spread0.046 · 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

Citations20
Published2016
Admission routes1
Has abstractyes

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