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Record W2290782450 · doi:10.4137/rpo.s20360

A Scoping Review of Clinical Practice Improvement Methodology Use in Rehabilitation

2016· review· en· W2290782450 on OpenAlexaff
Marie‐Ève Lamontagne, Cynthia Gagnon, Anne-Sophie Allaire, Luc Noreau

Bibliographic record

VenueRehabilitation Process and Outcome · 2016
Typereview
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsCégep de JonquièreUniversité LavalUniversité de SherbrookeCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsPopularityRehabilitationContext (archaeology)Process (computing)Clinical PracticeProcess managementQuality (philosophy)Quality managementPsychologyComputer scienceKnowledge managementMedicineManagement scienceOperations managementBusinessEngineeringNursingPhysical therapyGeographySocial psychology

Abstract

fetched live from OpenAlex

Context The Clinical Practice Improvement (CPI) approach is a methodological and quality improvement approach that has emerged and is gaining in popularity. However, there is no systematic description of its use or the determinants of its practice in rehabilitation settings. Method We performed a scoping review of the use of CPI methodology in rehabilitation settings. Results A total of 103 articles were reviewed. We found evidence of 13 initiatives involving CPI with six different populations. A total of 335 citations of determinants were found, with 68.7% related to CPI itself. Little information was found about what type of external and internal environment, individual characteristics and implementation process might facilitate or hinder the use of CPI. Conclusion Given the growing popularity of this methodological approach, CPI initiatives would gain from increasing knowledge of the determinants of its success and incorporating them in future implementation.

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)0.062
metaresearch head score (Gemma)0.197
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.062
Threshold uncertainty score0.329

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.197
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0060.005
Bibliometrics0.0320.036
Science and technology studies0.0020.003
Scholarly communication0.0070.006
Open science0.0030.004
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.824
GPT teacher head0.792
Teacher spread0.032 · 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 designSystematic review
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

Citations2
Published2016
Admission routes1
Has abstractyes

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