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Record W4410247667 · doi:10.3389/feduc.2025.1565920

The impact of sleep, mental health, and gender on academic performance in Canadian university students

2025· article· en· W4410247667 on OpenAlexaffabout
Tara Kuhn, Jennifer J. Heisz, Laura E. Middleton

Bibliographic record

VenueFrontiers in Education · 2025
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsResearch Institute for AgingMcMaster UniversityUniversity of Waterloo
Fundersnot available
KeywordsMental healthSleep (system call)PsychologyApplied psychologyMedical educationGerontologyComputer sciencePsychiatryMedicine

Abstract

fetched live from OpenAlex

Purpose To understand the independent and combined effects of sleep and mental health on academic performance, while also exploring gender differences. Methods A cross-sectional survey was distributed to undergraduate students at two Canadian universities in March 2022. Sleep quality and quantity was assessed using the Pittsburgh Sleep Quality Index. Mental health variables included stress, depression, and anxiety. Academic performance was self-reported as students’ cumulative percent average. Multiple linear regressions were used to investigate how (1) sleep, (2) mental health, (3) sleep and mental health together related to academic performance. These analyses were then repeated, stratified by gender. Results A total of 1,258 undergraduate students participated. While mental health and sleep duration predicted academic performance among the whole sample, there were important gender differences. In gender-stratified data, sleep quality and quantity predicted academic performance in men but not mental health in the combined model. For women, stress, depression, and anxiety predicted academic performance but not sleep quality. Sleep duration squared, but not sleep duration simply, was associated with academic performance in women. Conclusion Sleep and mental health are essential for academic performance in undergraduate students. Further, gender may play a critical role. Universities should consider gender-specific supports to improve the wellbeing of their students.

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 score0.058
Threshold uncertainty score0.944

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 head0.008
GPT teacher head0.330
Teacher spread0.322 · 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

Citations6
Published2025
Admission routes2
Has abstractyes

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