Voices for change: inclusion of lived experience self-injury research, practice, education, and advocacy
Bibliographic record
Abstract
Objective: Despite gains in research knowledge, self-injury remains unduly and widely stigmatised. This can preclude people with lived experience from playing active and important roles in the field. In this paper, we discuss how people with lived experience can offer vital contributions in this regard. Method: Position paper based on narrative review. Results: According to the current and especially recent literature in the field, people with lived experience of self-injury can play significant roles as researchers, educators, clinicians, and advocates. Conclusion: Given the unique perspectives and strength people with lived experience of self-injury have to offer, their contributions to the field need to be harnessed and championed. This requires concerted efforts to address stigma and otherwise unhelpful discourses. In doing so, a more inclusive field with greater representation of people with lived experience can be realised. This, in turn, is conducive to advancing our understanding of self-injury and promoting the wellbeing of all people with such lived experience.
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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 | 0.085 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".