Investigating the peer Mentor-Mentee relationship: characterizing peer mentorship conversations between people with spinal cord injury
Bibliographic record
Abstract
This study aimed to: (1) develop a coding manual to characterize topics discussed and conversation techniques used during peer mentorship conversations between people with spinal cord injury (SCI); (2) assess the reliability of the manual; and (3) apply the manual to characterize conversations. The study was conducted in partnership with three Canadian provincial SCI organizations. Twenty-five phone conversations between SCI peer mentors and mentees were audio-recorded and transcribed verbatim. Ten transcripts were inductively analyzed to develop a coding manual identifying topics and techniques used during the conversations. Inductive technique codes were combined and deductively linked to motivational interviewing and behaviour change techniques. Two coders independently applied the coding manual to all transcripts. Code frequencies were calculated. The coding manual included 14 topics and 31 techniques. The most frequently coded topics were personal information, recreational programs, and chronic health services for mentors and mentees. The most frequently coded techniques were giving personal information, social smoothers, and closed question for mentors; and giving personal information, social smoothers, and sharing perspective for mentees. This research provides insights into topics and techniques used during real-world peer mentorship conversations. Findings may be valuable for understanding and improving SCI peer mentorship programs.Implications for RehabilitationSCI peer mentorship conversations address a wide range of rehabilitation topics ranging from acute care to living in the community.Identification of the topics discussed, and techniques used in SCI peer mentorship conversations can help to inform formalized efforts to train and educate acute and community-based rehabilitation professionals.Identifying commonly discussed topics in SCI peer mentorship conversation may help to ensure that peer mentors are equipped with the necessary knowledge and resources, or the development of those resources be prioritized.Developing a method to characterize the topics discussed and techniques used during SCI peer mentorship conversations may aid in designing methods to evaluate how rehabilitation professionals provide support to people with SCI. SCI peer mentorship conversations address a wide range of rehabilitation topics ranging from acute care to living in the community. Identification of the topics discussed, and techniques used in SCI peer mentorship conversations can help to inform formalized efforts to train and educate acute and community-based rehabilitation professionals. Identifying commonly discussed topics in SCI peer mentorship conversation may help to ensure that peer mentors are equipped with the necessary knowledge and resources, or the development of those resources be prioritized. Developing a method to characterize the topics discussed and techniques used during SCI peer mentorship conversations may aid in designing methods to evaluate how rehabilitation professionals provide support to people with SCI.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".