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Record W2995949314 · doi:10.5204/ssj.v10i3.1412

Fall Break Fallout: Exploring Student Perceptions of the Impact of an Autumn Break on Stress

2019· article· en· W2995949314 on OpenAlexafffund
Michael Agnew, Heather Poole, Ayesha Khan

Bibliographic record

VenueStudent Success · 2019
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsMcMaster UniversityUniversity of OttawaConestoga College
FundersMinistry of Colleges and UniversitiesMcMaster University
KeywordsIntervention (counseling)Mental healthStress (linguistics)PerceptionPsychologyMedical educationApplied psychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

The mental health of post-secondary students has dominated recent discourse surrounding higher education. Accordingly, many institutions have introduced a break in the fall term, designed to support student well-being. As part of an interdisciplinary, longitudinal study examining the effects of the fall break on student stress, we held focus groups with undergraduates. We observed mixed feedback surrounding this intervention. Students appreciated the fall break as an opportunity to reduce their stress, yet they frequently reported negative impacts of the break on the timing of academic assessments and their ability to effectively manage study time. Based on extensive dialogue with students, we provide recommendations for institutions which have implemented or are considering implementing a fall break as a way to support student mental health. We aim to address the paucity of qualitative research on student stress and develop a deeper understanding of the factors driving students’ responses to stress intervention policies.

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 score0.011
Threshold uncertainty score0.922

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.376
Teacher spread0.348 · 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

Citations50
Published2019
Admission routes2
Has abstractyes

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