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Record W4407085755 · doi:10.1080/00049530.2025.2456728

Voices for change: inclusion of lived experience self-injury research, practice, education, and advocacy

2025· article· en· W4407085755 on OpenAlexaff
Penelope Hasking, Amanda Aiyana, Sophie Haywood, Kassandra Hon, Katrina Hon, Sylvanna Mirichlis, Kirsty Stewart, Adrienne Wilmot, Stephen P. Lewis

Bibliographic record

VenueAustralian Journal of Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Guelph
FundersNational Health and Medical Research CouncilSuicide Prevention AustraliaAustralian Government
KeywordsLived experienceInclusion (mineral)PsychologyField (mathematics)Social psychologyPedagogyPsychotherapist

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.168
GPT teacher head0.538
Teacher spread0.370 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations2
Published2025
Admission routes1
Has abstractyes

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