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Record W2186935626 · doi:10.1037/ort0000075

Suicide risk assessments: Examining influences on clinicians’ professional judgment.

2015· article· en· W2186935626 on OpenAlexaff
Cheryl Regehr, Vicki R. LeBlanc, Marion Bogo, Jane Paterson, Arija Birze

Bibliographic record

VenueAmerican Journal of Orthopsychiatry · 2015
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsCentre for Addiction and Mental HealthThe Wilson CentreUniversity of Toronto
Fundersnot available
KeywordsSuicidal ideationPsychologySuicide preventionClinical psychologyHuman factors and ergonomicsPoison controlRisk assessmentOccupational safety and healthInjury preventionCognitionMental healthPsychiatryMedicineMedical emergency

Abstract

fetched live from OpenAlex

Professional judgment in complex clinical situations such as the assessment of suicide risk encompasses a multifaceted cognitive understanding of the substantive issues, technical expertise, and emotional awareness. This experimental design study investigated the degree to which the previous work-related experiences of clinicians and their preexisting emotional state influence professional judgment regarding acute risk in patients presenting with suicidal ideation. Experienced social workers and social work students conducted suicide risk assessments on 2 standardized patients performing in scenarios constructed to depict individuals presenting with suicidal ideation. This study revealed significant variations in clinical judgments of practitioners assessing suicide risk. While scores on standardized risk assessment measures were the strongest predictor of judgments regarding the need for hospitalization to ensure the safety of the patient, other influences included clinician age and levels of posttraumatic stress symptoms. Mental health clinicians and organizations that employ them should be aware of possible individual influences on professional judgments related to suicide risk.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.068
GPT teacher head0.465
Teacher spread0.397 · 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 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".

Quick stats

Citations27
Published2015
Admission routes1
Has abstractyes

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