Using an integrated knowledge translation approach to inform a pilot feasibility randomized controlled trial on peer support for individuals with traumatic brain injury: A qualitative descriptive study
Bibliographic record
Abstract
INTRODUCTION: Traumatic brain injury (TBI) is estimated to affect 10 million people annually, making it a leading cause of morbidity and mortality worldwide. One cost-effective intervention that has been shown to minimize some of the negative sequelae after TBI is peer support. However, the evidence supporting the benefits of peer support for individuals with TBI is sparse and of low quality. Integrated knowledge translation (iKT) may be one approach to optimizing the evaluation of peer support programs among individuals with TBI. Therefore, the objectives are: (1) To understand key informants' perspectives of the barriers and facilitators of participating in peer support research and programs among individuals with TBI; (2) to understand key informants' perspectives on the perceived impacts of peer support programs on individuals with TBI; and, (3) to demonstrate how an iKT approach can inform the development and implementation of a pilot feasibility randomized controlled trial (RCT). METHODS: A qualitative descriptive approach using one-on-one semi-structured interviews was used. Purposive sampling of 22 key informants included 8 peer support mentors, 4 individuals with TBI who received peer support, 3 caregivers of individuals with TBI, 4 peer support program staff, and 3 academics in peer support and/or TBI. RESULTS: There were five main themes related to the barriers and facilitators to participating in peer support research and programs: knowledge, awareness, and communication; logistics of participating; readiness and motivation to participate; need for clear expectations; and matching. There were three main themes related to the perceived impact of peer support: acceptance, community, social experiences; vicarious experience/learning through others: shared experiences, role-modelling, encouragement; and "I feel better." Discussions with our Research Partner led to several significant adaptations to our trial protocol, including removing the twice/week intervention arm, shortening of the length of trial, and changing the measure for the community integration outcome. DISCUSSION/CONCLUSION: This is the first study to use an iKT approach to inform a trial protocol and the first to assess the barriers and facilitators to participating in peer support research.
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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 | 0.121 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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, 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".