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Record W4402812325 · doi:10.1080/00207411.2024.2406936

Canadian undergraduate students’ mental health literacy and its influence on psychological distress and help-seeking behaviour

2024· article· en· W4402812325 on OpenAlexaffabout
Karissa L. Horne, Kerry B. Bernes, Thelma M. Gunn, Daniel Balderson

Bibliographic record

VenueInternational Journal of Mental Health · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsMental health literacyMental healthPsychological distressHelp-seekingPsychologyDistressLiteracyMedical educationHealth literacyClinical psychologyPsychiatryMedicineMental illnessPedagogyHealth care

Abstract

fetched live from OpenAlex

Psychological distress (PD) among Canadian undergraduate students continues to be a prevalent issue. Developing an enhanced understanding of the relationship between mental health literacy (MHL), PD and help-seeking behavior (HSB) can provide researchers, educators, and clinicians with additional insight as to what knowledge should be taught and reiterated to undergraduate students that will support their mental health. While many studies across the globe have consistently reported significant positive relationships between MHL and HSB, there remains mixed findings regarding the relationship between MHL and PD. The purpose of this study was to explore Canadian undergraduate students’ degree of MHL, and the influence it has on their levels of PD and HSB. While previous Canadian studies have explored students’ MHL, this is the first Canadian study that has examined the relationships between MHL, PD and HSB. A total of 335 participants completed an online survey. More than half of the students reportedly experienced heightened levels anxiety, depression, and stress. The results suggested that Canadian undergraduate students demonstrate adequate levels of MHL and that some attributes of MHL have significant relationships with PD and HSB. Further research is warranted to strengthen our understanding of the influence of MHL on PD and HSB.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.695
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.465
Teacher spread0.433 · 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.

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
Published2024
Admission routes2
Has abstractyes

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