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Record W2939295159 · doi:10.1708/3104.30938

Psychometric properties of the Italian version of the Nurses’ Global Assessment of Suicide Risk (NGASR) scale

2019· article· en· W2939295159 on OpenAlexaff
Paolo Ferrara, Stefano Terzoni, Armando D’Agostino, John R. Cutcliffe, Yelissa Pozo Falen, Silvana Esther Corigliano, Loris Bonetti, Anne Destrebecq, Orsola Gambini

Bibliographic record

VenueRivista di psichiatria · 2019
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsWycliffe College
Fundersnot available
KeywordsSuicidal ideationMedicineRisk assessmentScale (ratio)PopulationPsychiatryPoison controlPsychometricsSuicide preventionClinical psychologyPsychologyEnvironmental health

Abstract

fetched live from OpenAlex

AIM: People with mental disorders have higher risk of suicide compared to the general population. Assessment of risk factors can help nurses reducing suicidal risk. The Nurses' Global Assessment of Suicide Risk scale (NGASR) has proven valid and reliable in supporting the nursing evaluation of suicidal risk in different studies. The aim of the study was to examine the psychometric properties of the NGASR in the Italian population. METHODS: We translated the scale and administered it to a sample of 121 patients admitted to acute psychiatric wards. RESULTS: The Content Validity Index-Scale (CVI-S) was 96.7%, the correlation with the Scale for Suicide Ideation (SSI) score was high (r=.98, p<.001). Inter-rater reliability (rho=.97, p<.001), and test-retest stability (p=.96) were satisfactory. Factor analysis pointed out 5 factors and the 15 items of the NGASR-ita explained 61.29% of total variance. Of the 121 subjects assessed upon admission, 25.62% had average or higher suicidal risk. DISCUSSION AND CONCLUSIONS: The use of valid screening tools in support of Suicide risk assessment is recommended. The NGASR-ita is a valid and reliable tool, suitable for nursing assessment of suicidal risk in the acute psychiatric setting.

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)0.000
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 score0.020
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.301
Teacher spread0.285 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueRivista di psichiatriaSame topicSuicide and Self-Harm StudiesFrench-language works237,207