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Record W4360600325 · doi:10.1097/jxx.0000000000000851

Feasibility of a universal suicidality tool for adolescents

2023· article· en· W4360600325 on OpenAlexaboutno aff
Rebecca Abaddi, LaVetta Pickens, Jade Burns, Mackenzie Adams, George H. Shade, Wayne W. Bradley, Elizabeth A. Duffy

Bibliographic record

VenueJournal of the American Association of Nurse Practitioners · 2023
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNurse practitionersMedicinePsychiatryMedical emergencyFamily medicineHealth carePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The suicide rate among adolescents has been increasing rapidly over the past several years. LOCAL PROBLEM: Adequate screening for suicide risk in this population, particularly youth of color, is lacking. METHODS: The Ask Suicide-Screening Questions (ASQ) tool was implemented at two adolescent-focused health clinics in a large U.S. city. INTERVENTIONS: This project followed the Ottawa Model of Research Use. Participating clinicians were surveyed before and after receiving an educational module on suicide risk screening, the ASQ tool, and clinical pathways. Clinicians were also asked about the feasibility and acceptability of the ASQ tool in their practice. An electronic medical records software was used to gather data on patients newly screened for suicide risk using the ASQ tool. RESULTS: Among eligible patients, 40.2% were screened using the ASQ tool during the 4-month duration of the project. Most clinicians reported that using the tool was feasible within their practice (66%) and 100% endorsed its acceptability (i.e., reporting that they were comfortable screening for suicide and that the ASQ was easy to use). CONCLUSIONS: The ASQ may be a promising screening tool for clinicians to use to address the mental health needs of at-risk youth. This project supports the universal acceptability and feasibility of its use in inner-city primary care clinics.

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)0.002
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 score0.032
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.369
Teacher spread0.331 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Association of Nurse PractitionersSame topicSuicide and Self-Harm StudiesFrench-language works237,207