Barriers and facilitators to implementing peer mentorship programs for individuals with spinal cord injury into rehabilitation hospitals: a multiple case study
Bibliographic record
Abstract
Purpose To identify and compare barriers and facilitators to implementing a spinal cord injury (SCI) peer mentorship program at two rehabilitation hospitals.Materials and Methods 24 participants from the two rehabilitation hospitals participated − 10 were from China and 14 were from Canada. Semi-structured interviews and focus groups were used to collect data. A cross-case analysis based on the Consolidated Framework for Implementation Research was conducted.Results At an individual level, four common facilitators for both hospitals were: engaging patients with SCI, engaging health professionals, high-level leaders providing financial and instrumental support, and increasing health professionals’ motivation to implement the program. Two common barriers were health professionals’ low capability and opportunity to implement the program. At an organizational level, one common facilitator was a team culture characterized by openness to innovation and a strong commitment to prioritizing patients’ needs. For the Canadian hospital, their partnership and connections with a community-based SCI organization and collaborative work infrastructure were facilitators. For the Chinese hospital, team separation within the local work infrastructure was a barrier.Conclusions Multiple barriers and facilitators to implementing SCI peer mentorship programs were identified in two culturally distinct contexts. Assessing organizational needs and identifying available resources are key pre-implementation processes for rehabilitation hospitals to implement SCI peer mentorship programs.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".