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Record W4402925091 · doi:10.1007/s44202-024-00228-0

Exploration of factors associated with complete mental health among postsecondary students

2024· article· en· W4402925091 on OpenAlexafffund
Abhinand Thaivalappil, Jillian Stringer, Andrew L. Wong, Ian Young, Alison Burnett, Andrew Papadopoulos

Bibliographic record

VenueDiscover Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsToronto Metropolitan UniversityUniversity of Guelph
FundersUniversity of Guelph
KeywordsMental healthPostsecondary educationPsychologyClinical psychologyGerontologyMedicinePsychiatryHigher educationPolitical science

Abstract

fetched live from OpenAlex

Postsecondary students are at risk for mental health problems and there has been an overall reduction in flourishing in recent years. The aim of this study was to explore factors associated with general mental health among students from one postsecondary institution (n = 1082). The Mental Health Continuum-Short Form was used as the outcome, which categorizes individuals as being languishing (i.e., state of incomplete mental health), moderately mentally healthy, or flourishing (i.e., positive mental health). A multivariable ordered logistic regression analysis was applied to identify factors associated with more favourable dimensions of mental health. Most students were moderately mentally healthy (53%), compared to flourishing (32%), and languishing (15%). Factors significantly associated with positive mental health included students who: (i) participated in meditation, (ii) participated in physical exercise, (iii) frequently experienced challenges that helped them grow, (iv) felt one's campus did enough to protect students from COVID-19, and (v) felt they were part of a campus that looked out for others. This provides further evidence for investing in mental health training for instructors, promoting positive coping strategies within the student population, campuswide implementation of a universal approach to health promotion, and building a sense of community.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.075
GPT teacher head0.420
Teacher spread0.345 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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