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Record W4401118882 · doi:10.1080/0309877x.2024.2385402

A pilot study of academic burnout and stress in undergraduate students: the role of canine-assisted interventions

2024· article· en· W4401118882 on OpenAlexaff
Corinne Syrnyk, Erin Williams, Alisa D. McArthur

Bibliographic record

VenueJournal of Further and Higher Education · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsSt. Mary's University
Fundersnot available
KeywordsBurnoutAttendancePsychologyPsychological interventionIntervention (counseling)Stress (linguistics)Clinical psychologyPerceived Stress ScaleScale (ratio)Mental healthDuration (music)Medical educationMedicinePsychiatry

Abstract

fetched live from OpenAlex

Burnout, often linked to increased stress, can impact student mental health, academic success, and overall well-being. To investigate animal-assisted interventions’ (specifically a canine-assisted intervention; CAI) impact on student stress and burnout, a free CAI event was held on campus prior to final exams (n = 41). Self-selecting participants completed the School Burnout Inventory (SBI; Salmela-Aro et al. 2009) and Perceived Stress Scale (PSS; Cohen, Kamarch, and Mermelstein 1983) before and after the event. Results showed a reduction in self-reported levels of stress and burnout from before to after the CAI. The impact of self-determined duration of attendance showed that those who attended the CAI for longer had a greater reduction in stress than did those who spent less time at the event. The findings suggest that CAI events can reduce perceived student burnout, alongside stress, strengthening arguments for CAIs utilisation in academic settings, and considers how the duration of CAI engagement may benefit different 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 score0.404
Threshold uncertainty score0.158

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 head0.032
GPT teacher head0.409
Teacher spread0.376 · 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 routes1
Has abstractyes

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