E-mentoring for youth with physical disabilities preparing for employment: a content analysis of support exchanged between participants of a mentored and non-mentored group
Bibliographic record
Abstract
Background: Peer-mentoring is a method of delivering support that may ameliorate some of the challenges that youth with physical disabilities experience when preparing for future employment. This qualitative study compared and described forum content of an employment-focused peer e-mentoring intervention for youth with physical disabilities with a focus on support provided within a mentored group (an experimental group) and a non-mentored group (a control group).Methods: Using a descriptive qualitative approach, textual data from discussion forums of two groups within a peer e-mentoring intervention were analyzed through a content analysis. This qualitative study was part of a larger mixed-method pilot-randomized control trial on peer e-mentoring.Results: The mentored group consisted of nine youth with physical disabilities, aged 15–21 (mean age, 17.8) and two paid-peer mentors, and the non-mentored group included seven youth with physical disabilities, aged 15–19 (mean age = 16.1). We analyzed 151 posts. The types of support exchanged differed between the two groups. Two overarching themes emerged: (1) solution-focused support and (2) catalysts for supportConclusion: Peer e-mentoring can offer youth unique forms of support to help them prepare for employment. Clinicians may explore the opportunity for peer e-mentoring to complement current practice in preparing youth with physical disabilities for future employment.Implications for rehabilitationClinicians such as social workers, occupational therapists, and life skill coaches who are interested in preparing youth with physical disabilities for employment should consider the unique types of support provided in an online-mentored group.Peer e-mentoring has the potential to offer youth with physical disabilities distinct types of support and addresses concerns raised from face-to-face mentoring programs, such as accessibility and time.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".