Protocol for a tailored self-compassion program for peer mentors within Canadian Spinal Cord Injury community service organizations
Bibliographic record
Abstract
Individuals who serve as peer mentors within Canadian spinal cord injury (SCI) community service organizations are reporting compassion fatigue from their roles. One psychological resource that has the potential to reduce compassion fatigue and promote overall mental health among peer mentors is self-compassion. However, there is limited knowledge of any self-compassion programs that have been tailored to address the needs of these individuals. To address this limitation, this paper outlines a protocol to examine the feasibility, acceptability, implementation, and effectiveness of a tailored self-compassion program to reduce compassion fatigue and enhance compassion satisfaction, self-compassion, and mental health among peer mentors within Canadian SCI community service organizations. The 6-week online program will be tested among 20 individuals (five separate groups of four) who serve as peer mentors within two Canadian SCI community service organizations. Feasibility and implementation data will be collected throughout the duration of the program and post program while acceptability data will be collected post program. To evaluate the effectiveness of the program, we will follow a single-group quasi-experimental design with surveys (pre/post/follow-up) and semi-structured interviews (post/follow-up). Results from this study are intended to inform the design of a self-compassion program for peer mentors within Canadian SCI community service organizations who experience compassion fatigue and reduced mental health. Providing evidence for the effectiveness of this program could prove useful for Canadian SCI community service organizations who wish to better support and retain their peer mentors and thus improve the quality of support they provide to mentees.
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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 | 0.042 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.121 | 0.019 |
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".