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Record W4394716036 · doi:10.1037/ser0000860

Remote mental health first aid training for correctional officers: A pilot study.

2024· article· en· W4394716036 on OpenAlexaff
Pamela Valera, Sarah Malarkey, Madelyn Owens, Noah Sinangil, Sanjana Bhakta, Tammy Chung

Bibliographic record

VenuePsychological Services · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsMental healthReferralMedicineIntervention (counseling)Focus groupTest (biology)DistressPsychologyClinical psychologyPsychiatryFamily medicine

Abstract

fetched live from OpenAlex

: 4.30, range 1-20) people incarcerated for mental health services. The reasons for referral included: "suicidal thoughts," "experiencing anxiety over being incarcerated during COVID," and "considering self-harm." A phenomenological approach was used to analyze the focus group meeting. The themes identified were: (a) COs experience with MHFA training was viewed positively (facilitators); (b) there is a need to improve mental wellness in correctional settings (barriers); and (c) mental health referral process for incarcerated individuals needs enhancement when implementing MHFA (barriers). MHFA training for COs is necessary to equip COs with the skills to safely support and refer incarcerated people experiencing a mental health crisis. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.168
GPT teacher head0.462
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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
Published2024
Admission routes1
Has abstractyes

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