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Record W2344948987 · doi:10.1177/0829573516645099

A Mental Health Training Format for Adult Education Teachers

2016· article· en· W2344948987 on OpenAlexaff
Fiona Meek, Jacqueline Specht, Susan Rodger

Bibliographic record

VenueCanadian Journal of School Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsWestern UniversityUniversity of Ottawa
Fundersnot available
KeywordsMental healthPsychologyMedical educationContext (archaeology)Focus groupNeeds assessmentPopulationMedicinePsychiatry

Abstract

fetched live from OpenAlex

The present study investigated the needs of adult education staff pertaining to adult students’ mental health issues within a local school board. The study utilized mixed-methods design and was divided into progression of three separate studies. An initial focus group was conducted to identify the 12 participants’ concerns and provide a direction for the needs assessment survey that was administered to the entire population of adult education teachers in the board. Two 2-hr workshops were designed for the 114 members of the staff based on the needs identified by the surveys. An evaluation of the workshops indicated that the workshops were valuable and further training was desired. By educating teachers about students who are learning in the context of mental health challenges, we will be able to provide them with the necessary tools to do their jobs more successfully and comfortably.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0400.009

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 head0.099
GPT teacher head0.445
Teacher spread0.346 · 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 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
Published2016
Admission routes1
Has abstractyes

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