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Record W7162083219 · doi:10.82308/43884

Knowledge brokers in Rehabilitation: who they are, how they are utilized, and how they are trained to improve evidence-based practice

2021· dissertation· en· W7162083219 on OpenAlexaboutno aff
Dina Gaid

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsKnowledge translationRehabilitationContext (archaeology)Health careWork (physics)UnderpinningBest practiceEvidence-based practice

Abstract

fetched live from OpenAlex

Despite available evidence to support optimal practices in rehabilitation, significant knowledge-practice gaps persist. Though knowledge brokers (KBs) can promote the uptake of research evidence to inform clinical practices, four major knowledge gaps were identified in the rehabilitation literature, potentially hindering their utilization. First, evidence on mechanisms underpinning KBs roles, and guidance on the type of support needed for successful implementation of these roles in rehabilitation contexts was scarce. Secondly, little was known about who KBs are, the type of work they do, and their training. Thirdly, no prior research has discussed the factors influencing the utilization of KBs to inform their employment within the rehabilitation sector. Lastly, the characteristics and content of educational training opportunities (ETO) offered to healthcare professionals who wish to undertake KBs roles, across Canada were unknown. Establishing a portrait of Canadian KBs working in the rehabilitation sector may inform health care organizations and knowledge translation specialists on how best to advance KBs’ practices.The overall objective of this thesis was to increase our knowledge about KBs to optimize their utilization in promoting the uptake of research evidence into rehabilitation clinical practice. Specifically, manuscript (1) aimed to highlight the differences and similarities between opinion leaders (OLs) and KBs with respect to context, mechanism, and outcomes. In particular, the objective was to describe the common patterns of OLs and KBs with respect to the context they work in, the mechanisms by which they impact outcomes, and the types of outcomes they influence. Manuscript (2) aimed to describe the profile of KBs working within rehabilitation settings in Canada, including the sociodemographic and professional characteristics, work activities, and training. Manuscript (3) aimed to identify the factors likely to promote or hinder the optimal use of KBs within rehabilitation settings, and manuscript (4) aimed to identify and describe current educational training opportunities (ETO) for KBs in Canada and to explore whether these programs meet the competencies needed for the KBs roles.This thesis is the first attempt to draw an overall portrait for KBs working in the rehabilitation sector in Canada. This portrait included KBs’ characteristics (personal and professional), roles and activities, factors influencing their roles, and cross-Canada training opportunities. The first manuscript created a context-mechanism-outcomes configuration which suggested the preferable features of OLs and KBs (e.g., being embedded in the organization, adequately skillful, and well-trained; performing the required roles; and using KT interventions adapted to the local context). The second manuscript highlighted that KBs are mostly expert clinicians who perform brokering activities on a part-time basis. Participants mostly perform linking agent, capacity builder, and information roles. Moreover, few participants received formal training to perform brokering activities. The third manuscript identified the individual, organisational and process level factors likely to hinder or promote the use of KBs including skillsets and networking abilities; culture, resources, and leadership support; and the need for specific training for KBs and for evaluation tools to monitor their performance. Lastly, the fourth manuscript suggested that ETO focused primarily on preparing participants with the research and knowledge brokering skills required to perform the capacity builder and evaluator roles. Comprehensive educational training covering all KBs roles and competencies are needed

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)0.065
metaresearch head score (Gemma)0.136
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.136
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0070.012
Scholarly communication0.0190.019
Open science0.0020.012
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0060.002

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 head0.307
GPT teacher head0.579
Teacher spread0.272 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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