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Record W1573636693 · doi:10.3233/wor-2010-1010

Knowledge brokering with injured workers: Perspectives of Injured Worker Groups and Health Care Professionals

2010· article· en· W1573636693 on OpenAlexafffund
Lynn Shaw, Joy McDermid, Anita Kothari, Rob Lindsay, Phil Brake, Peter Page, Colin Argyle, Crystal Gagnon, Melissa Knott

Bibliographic record

VenueWork · 2010
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsMcMaster UniversityWestern University
FundersCanadian Institutes of Health Research
KeywordsKnowledge transferKnowledge managementStakeholderParticipatory action researchInformation exchangeCitizen journalismProcess (computing)Health careKnowledge workerNursingMedicineBusinessMedical educationWork (physics)Public relationsComputer science

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study is to understand the barriers and facilitators in brokering knowledge brokering knowledge to help injured workers make informed decisions about recovery and to support their transitions to return to work (RTW). PARTICIPANTS: Perceptions of 63 Injured Worker Groups (IWGs) and 43 Health Care Professionals (HCPs) in facilitating and brokering knowledge were examined. METHODS: Critical theory and participatory action research approaches informed the development of a multi-stakeholder research team and the study design to support an exploration into knowledge exchange and transfer. Data was analyzed using a critical occupational perspective to reveal the source of barriers and to identify the facilitators of the knowledge exchange and transfer process. RESULTS: Barriers in transferring knowledge included system barriers, a lack of information accessibility, and problems with variations in injured worker capacity and experience using information. IWG and HCP participants lacked expertise in knowledge transfer. Findings also revealed the interactive knowledge transfer processes that IWGs and HCPs use to help injured workers understand and use information. CONCLUSIONS: Change is required to improve knowledge exchange and transfer of information for and to persons with injuries and disabilities. Suggested changes include the development of a sustainable knowledge transfer community of practice, a best practice guide for knowledge brokers such as IWGs and HCPs, and a process for ongoing assessment and evaluation of injured worker information needs and preferences.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.085
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.048
GPT teacher head0.469
Teacher spread0.421 · 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

Citations18
Published2010
Admission routes2
Has abstractyes

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