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Record W6986470397

Police Crisis Negotiators’ Experiences Engaging Persons in Suicidal Crisis

2023· article· en· W6986470397 on OpenAlexaboutno aff

Bibliographic record

VenueScholarWorks (Walden University) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisNegotiationInterpersonal communicationQualitative researchCrisis interventionCriminal justicePoison controlSuicide preventionHuman factors and ergonomics
DOInot available

Abstract

fetched live from OpenAlex

Police are commonly in contact with persons in suicidal crisis. Individuals endorsing high-acuity suicidality present distinct challenges, including the capacity for self-directed lethal behavior potentially exacerbated by heightened emotionality, mental illness, homicidal ideation, substance impairment, and access to lethal means. Due to elevated public safety risks, a specialized police response is essential, with police crisis negotiators serving a vital function in managing and attaining peaceable outcomes. As a specialty role within policing that focuses on sustaining human life in significantly adverse circumstances, crisis negotiation with persons endorsing high-acuity suicidality merits more scholarly attention. There is a recognized paucity of research on negotiator experiences involving this population, generally and specifically from a Canadian perspective. This qualitative study explored negotiators’ operational experiences engaging with persons in suicidal crisis. Procedural justice theory and Peplau’s theory of interpersonal relations guided the research. Semistructured interviews of eight negotiators from Ontario, Canada, procured rich, descriptive information about these experiences. Thematic analysis generated four predominant themes: navigating through uncertainty, stewardship, humanizing the experience, and interdependence. Positive social change implications include yielding insights to enhance negotiator responses in preserving human life in these elevated-risk incidents.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.285
Teacher spread0.256 · 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 designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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