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Record W3033115997 · doi:10.3138/ptc-2019-0036

Using Mental Health First Aid Training to Improve the Mental Health Literacy of Physiotherapy Students

2020· article· en· W3033115997 on OpenAlexvenueno aff
Susan Edgar, Joanne Connaughton

Bibliographic record

VenuePhysiotherapy Canada · 2020
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsnot available
Fundersnot available
KeywordsMental health literacyMental healthPreparednessPopulationMental illnessPsychologyStigma (botany)MedicineNursingPsychiatry

Abstract

fetched live from OpenAlex

Purpose: Mental Health First Aid (MHFA) training has been proven to improve the literacy of trainees and reduce the stigma they may have toward individuals with mental health problems in the general population. Our research was designed to determine whether MHFA training had an impact on physiotherapy students’ attitudes toward psychiatry and mental illness, their confidence to engage with people with mental health problems, and their preparedness for practice. Method: Final-year students from one university who had finished MHFA training completed a questionnaire that included the Attitudes Toward Psychiatry–30 and questions about their perceived confidence in working with people with mental illness and preparedness for practice. Their responses were compared with those from a previous cohort of students at the same point in their university education who had not completed MHFA training. Results: The students who had completed MHFA training (response rate 83%) had a more positive attitude toward psychiatry and mental illness than those who had not ( p < 0.001). Their confidence in treating people with mental health problems also increased, and to a statistically significant extent ( p < 0.001). Conclusions: MHFA training appeared to improve students’ attitudes toward psychiatry and mental health, increase their confidence in treating people with mental health problems, and better prepare them for practice.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.048
GPT teacher head0.439
Teacher spread0.391 · 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 designNot applicable
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

Citations11
Published2020
Admission routes1
Has abstractyes

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