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Record W2978048479 · doi:10.1108/jpmh-01-2019-0014

When language is maladaptive: recommendations for discussing self-injury

2019· article· en· W2978048479 on OpenAlexaff
Penelope Hasking, Stephen P. Lewis, Mark Boyes

Bibliographic record

VenueJournal of Public Mental Health · 2019
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsTerminologyCoping (psychology)Framing (construction)PsychologyOriginalityPoison controlMental healthHuman factors and ergonomicsSuicide preventionInjury preventionSocial psychologyPsychotherapistDevelopmental psychologyClinical psychologyCognitive psychologyMedicineMedical emergencyLinguistics

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to call on researchers and clinicians to carefully consider the terminology used when discussing non-suicidal self-injury (NSSI), and specifically the use of the term “maladaptive” coping. Design/methodology/approach Drawing on literature regarding stigma, language and self-injury to support the argument that the term maladaptive is inappropriate to describe self-injury. Findings Use of the term maladaptive conflates short-term effectiveness with long-term outcomes and ignores context in which the behaviour occurs. Social implications Use of the term maladaptive to describe self-injury can invalidate the person with a history of NSSI, impacting stigma and potentially help-seeking. An alternate framing focussed on specific coping strategies is offered. Originality/value Language is a powerful medium of communication that has significant influence in how society shapes ideas around mental health. In proposing a change in the way the authors’ talk about self-injury there is potential to significantly improve the wellbeing of people with lived experience of self-injury.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.313
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.063
GPT teacher head0.407
Teacher spread0.344 · 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
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

Citations16
Published2019
Admission routes1
Has abstractyes

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