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Record W2062739803 · doi:10.4236/ojtr.2014.22013

Exploring Factors in the Systematic Use of Outcome Measures: A Multi-Disciplinary Rehabilitation Team Perspective

2014· article· en· W2062739803 on OpenAlexafffund
Diana Zidarov, Lise Poissant

Bibliographic record

VenueOpen Journal of Therapy and Rehabilitation · 2014
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsInstitut de Readaptation Gingras Lindsay de MontrealCentre for Interdisciplinary Research in RehabilitationUniversité de Montréal
FundersRéseau Provincial de Recherche en Adaptation-Réadaptation
KeywordsRehabilitationMultidisciplinary approachOutcome (game theory)Quality managementProcess managementPerspective (graphical)Process (computing)Focus groupMedical educationPsychologyMedicineQualitative researchNursingApplied psychologyOperations managementPhysical therapyComputer scienceEngineeringBusinessManagement systemPolitical science

Abstract

fetched live from OpenAlex

Purpose: The implementation of routine outcome measurement was initiated as a quality improvement initiative in a unit delivering intensive functional rehabilitation for people with lower limb amputation. Two years post-implementation, completion rates remained low which raised the need to gain an indepth understanding of the factors that might impact the systematic use of Outcome Measures (OMs). Method: A qualitative exploratory study embedded in the ongoing quality improvement initiative was designed. Data were gathered through a focus group with members of a multidisciplinary rehabilitation team. A deductive content analysis was performed using Consolidated Framework for Implementation Research (CFIR) as a guide to explore factors that impact routine outcome measurement. Results: Respondents perceived OMs as valid and offering clear advantages in clinical practice. At the organizational level, lack of fit with clinical practice, loss of project leaders and lack of clear management directives had negative repercussions on the use of OMs. Conclusion: Our results suggest that a dedicated project leader throughout the implementation process and effective communication may contribute to bypassing barriers associated to practice changes leading to a more systematic use of OMs among clinicians.

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)0.004
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.210
GPT teacher head0.375
Teacher spread0.165 · 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 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

Citations4
Published2014
Admission routes2
Has abstractyes

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