Factors Influencing the Uptake of Canadian Research Findings into the Care of Children with Arthritis: A Healthcare Provider Perspective
Bibliographic record
Abstract
OBJECTIVE: To determine barriers and facilitators to the uptake of findings from the Research in Arthritis in Canadian Children emphasizing Outcomes (ReACCh-Out) study into clinical care by pediatric rheumatologists (PR) and allied health professionals (AHP) caring for children with juvenile idiopathic arthritis (JIA) in Canada. METHODS: PR and AHP participated in this qualitative study through telephone interviews. Interview guides were developed using the Theoretical Domains Framework and focused on the use of information from the ReACCh-Out study in the practice of counseling patients and families. A directed content analysis approach was used for coding. RESULTS: Nineteen interviews (8 PR and 11 AHP) were completed. All PR had knowledge of the ReACCh-Out study. Three major themes were identified: (1) both groups are motivated to use information from research in clinical care; (2) volume and emotional effect of information on families are barriers; and (3) specific timepoints in care trigger providing this information. AHP had less knowledge of the ReACCh-Out study, did not feel it was their primary role to provide this information, and have a desire for more opportunity to participate in academic forums related to research. CONCLUSION: We have described a comprehensive overview of the barriers and facilitators perceived by healthcare providers in the translation of knowledge from JIA research into use in clinical practice. These findings provide a foundation for the development of knowledge translation strategies in the care of children with JIA and other rheumatic diseases.
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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 | 0.040 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".