Peer mentors’ experiences of delivering peer support for individuals with eating disorders: giving back and supporting processes of change
Bibliographic record
Abstract
Peer support is a promising approach to increasing hope, engagement, and connection for those with eating disorders (EDs). Emerging literature explores peer mentors' experiences of providing support, suggesting that mentors often benefit from providing peer support, particularly when well trained and supervised. We conducted semi-structured interviews or focus groups with 15 individuals providing peer support (one-on-one, group, or chat) to individuals with EDs. We identified 3 themes using reflexive thematic analysis (RTA) through a critical realist lens. Participants emphasized the importance of ongoing training and support to help them deliver high-quality peer support. They highlighted the importance of value-alignment in this work in terms of organizational valuing of lived experience and alignment with social justice. Participants reflected on how doing this work contributed to a sense of "giving back" and providing the kind of support they wished they had experienced. Providing peer support was described as emotion work; a challenging and rewarding experience for peer mentors. Findings carry implications for integrating peer support into the continuum of care for EDs, providing insight into approaches that can support peer support delivery in a way that promotes safety for those providing and receiving it.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".