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Record W4413907383 · doi:10.1142/s2810968625500019

Wounded Healers: Peer Support Work of Sexual Minority Men in Substance Use Recovery in Singapore

2025· article· en· W4413907383 on OpenAlexaff
Maha Yewtuck See, Chuanfei Chin, Daniel Weng Siong Ho, Rayner Kay Jin Tan

Bibliographic record

VenueCounselling & Psychotherapy Review Singapore · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsSubstance usePeer supportPsychologyPsychiatryMedicinePsychotherapist

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.356
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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