Transformational mentoring: Leadership behaviors of spinal cord injury peer mentors.
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to investigate the leadership behaviors of spinal cord injury (SCI) peer mentors and examine whether behaviors of peer mentors align with the tenets of transformational leadership theory. METHOD: A total of 12 SCI peer mentors aged 28-75 (M = 49.4) who had between 3 and 56 years (M = 13.9) of mentoring experience were recruited for the study. Utilizing a qualitative methodology (informed by a social constructionist approach), each mentor engaged in a semistructured interview about their experiences as a peer mentor. Interviews were transcribed verbatim and subjected to a directed content analysis. RESULTS: SCI peer mentors reported using mentorship behaviors and engaging with mentees in a manner that closely aligns with the core components of transformational leadership theory: idealized influence, inspirational motivation, individualized consideration, and intellectual stimulation. A new subcomponent of inspirational motivation described as 'active promotion of achievement' was also identified and may be unique to the context of peer mentorship. CONCLUSIONS: SCI peer mentors inherently use behaviors associated with transformational leadership theory when interacting with mentees. The results from this study have the potential to inform SCI peer mentor training programs about specific leadership behaviors that mentors could be taught to use and could lead to more effective mentoring practices for people with SCI. (PsycINFO Database Record
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".