Peer Work and Stigma Reduction in Australian Mental Health Policy: Tackling Sanism or Reinforcing the Status Quo?
Bibliographic record
Abstract
Peer workers are increasingly included as part of mental health policy approaches to stigma, reflecting ongoing imperatives to include lived experience within mental health policy and practice. Using a post-structural analysis of Australian mental health policy, we critically examine the effects of such inclusion on dominant enactments of peer work and stigma. We find that mental health policy predominantly produces stigma as a problem of individual lack of capacity and responsibility, reinforcing neoliberal and psychiatric logic that locate individuals as the site for intervention and mental health practitioners as the experts to undertake such interventions. The inclusion of peer workers, predominantly enacted as role models, promotes the appearance of progressive governance whilst distracting from the socio-material conditions and processes that mark individuals as other, and leads to significant harm when individuals seek support. Dominant enactments of stigma thus remain undisturbed by the inclusion of peer work within mental health policy. Our findings challenge the notion of inclusion of lived experience via the peer workforce as universally progressive, calling for a more nuanced examination of the effects of inclusion. We propose sanism as an alternative problematisation that aligns more closely with peer work and social justice. We conclude with practice and research recommendations.
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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 | 0.030 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.039 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".