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Record W2161954364 · doi:10.12927/hcq.2010.21817

Skill Development and Capacity Building Program for Best Practices in Rehabilitation

2010· article· en· W2161954364 on OpenAlexaff
Sherra Solway, Karima Velji

Bibliographic record

VenueHealthcare Quarterly · 2010
Typearticle
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsToronto Rehabilitation Institute
Fundersnot available
KeywordsBest practiceRehabilitationCapacity buildingMedical educationProcess managementNursingMedicinePsychologyBusinessPhysical therapyPolitical science

Abstract

fetched live from OpenAlex

There is a growing body of literature on evidence-based decision-making and best practice development and the skills required for these approaches to influence decisions. A skill development and capacity building (SDCB) program was implemented in 2004 to facilitate the application of clinical best practices in a hospital specializing in adult rehabilitation and complex continuing care. This article describes the pilot program and its evaluation and provides a five-year review of initiatives developed as a result of this program. This innovative program facilitated cross-learning, integration of research, education and practice and brought about positive change for clinical best practice. This program may serve as a model to facilitate best practice and knowledge translation in other healthcare environments by supporting and assisting clinicians in attaining the skills necessary for clinical best practice.

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)0.020
metaresearch head score (Gemma)0.030
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: Other · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0030.009
Research integrity0.0010.003
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 head0.063
GPT teacher head0.426
Teacher spread0.363 · 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
GenreOther

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

Citations0
Published2010
Admission routes1
Has abstractyes

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