Creative nonfiction approach to explore peer mentorship for individuals with spinal cord injury.
Bibliographic record
Abstract
PURPOSE: Research has examined peer mentorship to understand how it may help people with spinal cord injury (SCI) adapt and thrive. We still lack an in-depth understanding of the perspectives of SCI peer mentors and mentees on their dyadic relationship. This study was to explore the dyadic interactions and relationships between SCI peer mentors and mentees in a peer mentorship program delivered at a rehabilitation center. RESEARCH METHOD: = 12). Data were analyzed using a creative nonfiction approach. RESULTS: Three unique dialogical stories were developed. Story 1 (A slow and steady start) described how mentors took a mentee-centered approach in building the relationship. Story 2 (Mentorship and friendship: negotiating the "grey zone") highlighted how mentees and mentors experienced challenges in navigating the boundaries between mentorship and friendship. Story 3 (The "endless" job for mentor) showcased how the relationship could enter a phase in which it could affect mentors' well-being. CONCLUSIONS: The stories highlighted important attributes to the relationships between SCI mentors and mentees. Considerations were suggested for community-based SCI organizations to integrate peer mentorship into rehabilitation settings, including optimizing mentorship introductions and matching, defining mentors' role explicitly, and building support systems for mentors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| 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".