Quality peer mentorship in spinal cord injury: A thought-listing technique to understand characteristics of high-quality and low-quality peer mentors
Bibliographic record
Abstract
Background: Peer mentorship is a promising approach to support full participation in individuals living with spinal cord injury (SCI). Peer mentorship occurs when a peer mentor with lived experience of SCI provides knowledge, counsel and/or guidance to a mentee living with SCI. Little is known about strategies peer mentors use to support mentees. Understanding characteristics that differentiate high-quality from low-quality peer mentors may provide insight into the mechanisms that underlie quality peer mentorship. Objective: The aim of this study is to understand characteristics that differentiate high-quality and low-quality peer mentors in hospital and community settings. Methods: A thought-listing technique was completed by 25 peer mentors and 18 mentees (mean age: 46.7 years +/- 11.84; 48% female). Participants were asked to visualize quality peer mentors in hospital and community settings. Participants were then prompted to freely list any characteristics thought of during visualization. Final lists of characteristics were screened for duplicates and synonyms. Refined lists were thematically analyzed inductively to create distinct categories. Results: After screening, 276 characteristics (50% hospital-setting) were listed. The most frequently identified themes of effective peer mentors were similar in hospital and community settings which included: "knowledgeable", "listening ability" and "empathetic". Identified themes of ineffective peer mentors in hospital settings included "poor listening ability" and "judgmental"; and "rude" and "aggressive" in community settings. Implications: Ensuring all peer mentors are knowledgeable, empathetic and have strong listening skills may improve their ability to provide quality peer mentorship. Findings from this study will inform future training for peer mentorship programs.Acknowledgments: Social Sciences and Humanities Research Council; Michael Smith Foundation for Health Research
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".