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Record W2418061097 · doi:10.1111/jpm.12310

Effective ingredients of verbal de‐escalation: validating an English modified version of the ‘De‐Escalating Aggressive Behaviour Scale’

2016· article· en· W2418061097 on OpenAlexafffund
V. Mavandadi, Peter Bieling, Victoria Madsen

Bibliographic record

VenueJournal of Psychiatric and Mental Health Nursing · 2016
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsSt. Joseph’s Healthcare HamiltonMcMaster University
FundersMcMaster UniversitySt. Joseph's Healthcare Hamilton
KeywordsConstruct (python library)Scale (ratio)PsychologySeclusionConsistency (knowledge bases)Intervention (counseling)GermanApplied psychologySocial psychologyComputer sciencePsychiatryArtificial intelligenceLinguistics

Abstract

fetched live from OpenAlex

WHAT IS KNOWN ON THE SUBJECT?: Verbal de-escalation is an intervention aimed at calmly managing an agitated client to prevent violence. Effective de-escalation can help reduce the use of seclusion and restraint in psychiatric settings. Despite its importance in practice, there is little agreement on the necessary techniques of de-escalation and most of the research on the topic is based on expert opinion. To our knowledge, only one attempt at quantifying de-escalation skill has been pursued through the German-language De-Escalating Aggressive Behaviour Scale (DABS). While the DABS identified seven qualities necessary for de-escalation, it has not been validated in English and may lack important descriptors. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: The present study enhanced the original DABS with best, acceptable and least desirable staff de-escalation practice descriptions for each of the seven items. This enhancement of the DABS lead to the creation of the English modified DABS (EMDABS). The EMDABS was psychometrically validated for use in research and practice: raters could use the EMDABS with a high level of agreement and consistency. Also, the scale appeared to measure a single cohesive construct - de-escalation. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: With further validation, the EMDABS has potential to be the first English quantitative measure of de-escalation. The EMDABS offers seven items, with associated best practice descriptions, that may be used to inform de-escalation practice. The EMDABS can be used to evaluate training and education programmes and inform how these programmes and independent de-escalation practice may be improved. ABSTRACT: Introduction Verbal de-escalation is crucial to a non-coercive psychiatric environment. Despite its importance, the literature on de-escalation is sparse and mostly qualitative. To address this, Nau et al. (2009) quantified de-escalation by creating the German-language De-Escalating Aggressive Behaviour Scale (DABS). The DABS provides seven skills necessary for de-escalation, however it has not been validated in English and lacks the necessary anchor descriptions to make it useful. Aim To modify the DABS to include descriptions of best, acceptable and least desirable staff practice and to validate the English modified DABS (EMDABS). Method To develop item descriptions for the EMDABS, 50 conflictual staff-patient interactions were reviewed, summarized and cross-referenced with the literature (n = 19). Three raters then used the EMDABS to evaluate 272 simulations depicting these interactions. Results The EMDABS demonstrated very good inter-rater reliability [ICC (3, 1) = 0.752] and strong internal consistency (α = 0.901). A factor analysis revealed that the seven items were best represented by a single factor. Discussion The EMDABS was validated for future use in research and practice. Additional validation and future research directions are discussed. Implications for practice The EMDABS holds promise as a quantitative measure of de-escalation. Its seven items and best practice guidelines have clinical implications for improving practice and training.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.037
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.071
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.013
GPT teacher head0.366
Teacher spread0.352 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations54
Published2016
Admission routes2
Has abstractyes

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