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Record W1919083533 · doi:10.61611/2165-4611.1061

Mental Health First Aid USA: The Implementation of a Mental Health First Aid Training Program in a Rural Healthcare Setting

2014· article· en· W1919083533 on OpenAlexaboutno aff
Andrew O'Neill, Valerie Lester Leyva, Michael N. Humble, Melinda L. Lewis, John Addy S. Garcia

Bibliographic record

VenueContemporary Rural Social Work Journal · 2014
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthSocial workCertificationHealth careFirst aidMental illnessNursingMental healthcarePsychologyQuarter (Canadian coin)MedicineMedical educationPsychiatryPolitical scienceMedical emergency

Abstract

fetched live from OpenAlex

Nearly one-quarter of adults in the United States suffer from a documented mental disorder. Consequently, anyone could encounter a person with symptoms of mental illness at some point as they carry out their daily life activities. Although laypersons may accurately identify physical illnesses, they may lack necessary skills to identify symptoms of mental disorders, or know how to adequately respond to persons in a mental health crisis. Mental Health First Aid USA is an evidence-based certification program designed to teach lay citizens to recognize certain symptoms of common mental illnesses, offer and provide first aid assistance, and guide a person toward appropriate services and other support. The program targets a broad audience, from teachers, police officers, clergy members, and healthcare professionals to the average citizen volunteer. This practice note describes a pilot implementation of Mental Health First Aid USA by a social worker at a rural hospital in Central California. The process and results of program implementation are discussed as well as implications for social work practice in rural healthcare settings.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.433
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.062
GPT teacher head0.413
Teacher spread0.351 · 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.

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

Citations1
Published2014
Admission routes1
Has abstractyes

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