Wounded Healers: Peer Support Work of Sexual Minority Men in Substance Use Recovery in Singapore
Bibliographic record
Abstract
Integrating peer support for substance use treatment has been associated with multiple benefits for individuals who are in recovery. There is a high need for trained peer supporters in Singapore’s policy environment, particularly for LGBTQ[Formula: see text] individuals who require access to safe spaces for substance use recovery, of which there are insufficient trained professionals and minimal training in this intersection of substance use recovery for LGBTQ[Formula: see text] individuals. Peer support is thus essential to provide appropriate, safe, and evidence-based support. The article examines the indispensable nature of peer support in addiction recovery and the value of working with one’s traumatic experiences in the process of helping others. This is done through examining a continuing education (CE) programme, The Wounded Healer, developed and implemented for a peer support programme in a substance use recovery centre (The Greenhouse Community Services). A conceptual background on the wounded healer will be introduced, followed by a presentation of the three modules of the programme: (a) the gifts of wounded healers, (b) self-compassion for the wounded healer, and (c) safeguards for wounded healers. Discussions and reflections will cover peer support work as a healing process, the value of CE, and public health implications of peer support.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".