Knowledge brokering with injured workers: Perspectives of Injured Worker Groups and Health Care Professionals
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".