Supporting meaningful research partnerships: an interview study applying behavior change theory to develop relevant recommendations for researchers
Bibliographic record
Abstract
Research partnerships, while promising for ensuring translation of relevant and useable findings, are challenging and need support. This study aimed to apply behavior change theory to understand and support researchers' adoption of a research partnership approach and the Integrated Knowledge Translation (IKT) Guiding Principles for conducting and disseminating spinal cord injury (SCI) research in partnership. Using an IKT approach, SCI researchers across Canada and the USA completed a survey (n = 22) and were interviewed (n = 13) to discuss barriers and facilitators to deciding to partner and follow the IKT Guiding Principles. The Behaviour Change Wheel, Theoretical Domains Framework (TDF), and Mode of Delivery Ontology were used to develop the survey, interview questions, and guided analyses of interview data. COM-B and TDF factors were examined using descriptive statistics and abductive analyses of barriers and facilitators of decisions to partner and/or use the IKT Guiding Principles. TDF domains from the interview transcripts were then used to identify intervention, content, and implementation options. 142 factors (79 barriers, 63 facilitators) related to deciding to partner, and 292 factors (187 barriers, 105 facilitators) related to deciding to follow the IKT Guiding Principles were identified. Barriers to partnering or use the IKT Guiding Principles were primarily related to capability and opportunity and relevant intervention options were recommended. Interventions must support researchers in understanding how to partner and use the IKT Guiding Principles while navigating a research system, which is not always supportive of the necessary time and costs required for meaningful research partnerships.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.071 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".