Delivery of a community-based peer mentorship program for people with spinal cord injury at a rehabilitation center
Bibliographic record
Abstract
Introduction: Community-based spinal cord injury (SCI) organizations deliver peer mentorship programs in rehabilitation settings. Little is known on how these programs are delivered through the collaboration between community-based SCI organizations and rehabilitation institutions. This study aimed to identify barriers, facilitators, and collaboration processes within a SCI peer mentorship program provided by a community-based organization at a rehabilitation center. Methods: A qualitative case study design was applied. Seven participants were recruited, including two mentees, two mentors, one program director of the community-based SCI organization, and two healthcare professionals of the rehabilitation center. Each participant completed a one-on-one interview. Data were analyzed inductively and deductively based on the Consolidated Framework for Implementation Research (CFIR). Results: Ten factors were identified to influence the delivery of the peer mentorship program, including nine CFIR constructs. Successful delivery of the program required strong, collaborative inter-professional relationships between health professionals and community organizational staff (e.g., peer mentors) as facilitators; whereas potential cost, minimal patient needs, and limited mentor resources were found to be barriers. Engaging health professionals by initiating communications, reflecting and evaluating the program collectively with health professionals were important collaboration processes for the community-based organization to maintain effective partnership with the rehabilitation center. Discussion: The collaboration processes and strategies to addressing/leveraging the barriers and facilitators may inform evidence-based practice to establish and optimize the delivery of SCI peer mentorship programs in various rehabilitation settings.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".