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Record W4412109732 · doi:10.1177/13634615251327886

Gaps and gains in parents’ mental health literacy: A cross-cultural comparison on attention-deficit/hyperactivity disorder

2025· article· en· W4412109732 on OpenAlexaffabout
Janet W. T. Mah, Wendy Li

Bibliographic record

VenueTranscultural Psychiatry · 2025
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsBC Children's HospitalUniversity of British Columbia
Fundersnot available
KeywordsPsychoeducationMental healthMental health literacyAttention deficit hyperactivity disorderStigma (botany)Ethnic groupPsychologyIntervention (counseling)LiteracyClinical psychologyPsychiatryMental illnessMedicine

Abstract

fetched live from OpenAlex

The aim of this study was to compare the knowledge and beliefs about attention-deficit/hyperactivity disorder (ADHD) between first-generation Chinese Canadian and European Canadian parents, and to evaluate the efficacy of an existing translated ADHD information resource to improve their ADHD literacy. A community sample of 28 first-generation Chinese Canadian and 28 European Canadian parents of elementary school-aged children completed questionnaires measuring ADHD knowledge, treatment attitudes and perceived stigma, both before and after reading an informational pamphlet about ADHD. Compared with European Canadian parents, Chinese Canadian parents initially had less knowledge and more stigma about ADHD but had greater increases in knowledge and medication acceptability after passive psychoeducation. For both groups, passive psychoeducation increased ADHD stigma, but did not impact the already high acceptability for parent training intervention. Passive psychoeducation can improve mental health literacy among ethnic minority groups, but further work needs to tailor resources to address specific gaps in mental health literacy and recognize the need for concurrent stigma-reduction methods.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.407
Teacher spread0.374 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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