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Record W4410906443 · doi:10.47678/cjhe.v55i2.190351

Mental Health and Foundational Academic Behaviours: Pieces of the Academic Success Puzzle

2025· article· en· W4410906443 on OpenAlexafffundvenue
Meg Kapil, Ramin Rostampour, Allyson F. Hadwin, Mariel Miller, Stuart Macdonald

Bibliographic record

VenueCanadian Journal of Higher Education · 2025
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyMental healthLearning developmentAcademic achievementMathematics educationHigher educationPedagogyPolitical sciencePsychotherapist

Abstract

fetched live from OpenAlex

The interplay between mental health and academic behaviours has been understudied. This study examined the relationship between foundational academic behaviours (e.g., attending class and meeting assignment deadlines), student mental health and well-being, and academic performance. Participants consisted of 229 students (52.6% female) who participated in a first-year introductory learning-to-learn course. Findings from structural equation modelling indicated: (a) higher levels of foundational academic behaviours predicted higher GPA, (b) higher levels of emotional well-being predicted higher levels of foundational academic behaviours and higher GPA, and (c) foundational academic behaviours mediated the relationship between emotional well-being and GPA. Findings affirm the integral role of mental health in academic performance and highlight the mediating role of foundational academic behaviours in this relationship. The association between emotional well-being and foundational academic behaviours underscores the multifaceted nature of academic performance and the importance of considering both mental health and foundational academic behaviours in academic success. Findings from this study suggest that managing behaviours that facilitate engagement in academic tasks, and mental health as a potential internal condition for learning, are both important pieces to the academic success puzzle.

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 score0.147
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.027
GPT teacher head0.402
Teacher spread0.375 · 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
Published2025
Admission routes3
Has abstractyes

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