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Fostering evidence-based practice in community-based rehabilitation: Strategies for implementation

2015· article· en· W2154560531 on OpenAlexaff
Helen Buchanan, Theresa Lorenzo, Mary Law

Bibliographic record

VenueSouth African Journal of Occupational Therapy · 2015
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRehabilitationOccupational therapyEvidence-based practiceCommunity practiceCommunity-based rehabilitationWork (physics)Medical educationCommunity of practicePsychologyNursingMedicinePhysical therapyAlternative medicinePedagogyEngineering

Abstract

fetched live from OpenAlex

Occupational therapists around the world are taking up the challenge to implement an evidence-based practice approach to the development of occupational therapy services. The emphasis in applying evidence-based practice within occupational therapy has been strongly biomedical in focus. In South Africa, many occupational therapists work in communities where their work is largely community-based rehabilitation. With no examples of how evidence-based practice can be applied in such settings, therapists have struggled with how it may be used to inform their practice. This paper explores the concepts of evidence-based practice and community-based rehabilitation, and illustrates how evidence-based practice can be applied within community-based rehabilitation. Examples are provided to show how evidence-based practice can realistically be applied in community-based rehabilitation programmes with the intention of empowering therapists to begin using evidence as a basis for their practice. It further explores how evidence-based practice can be used by occupational therapists to inform decision-making related to the development of community-based rehabilitation programmes and services.Key words: evidence-based practice, community-based rehabilitation, occupational therapy, practice-based evidence

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)0.011
metaresearch head score (Gemma)0.004
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 score0.452
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.522
GPT teacher head0.585
Teacher spread0.063 · 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 designQualitative
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

Citations1
Published2015
Admission routes1
Has abstractyes

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