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Record W4206116485 · doi:10.31219/osf.io/xba4v

Post-Secondary Student Stress: A Qualitative Descriptive Study (Preprint)

2020· preprint· en· W4206116485 on OpenAlexaff
Brooke Linden, Heather Stuart

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicHealthcare professionals’ stress and burnout
Canadian institutionsQueen's University
Fundersnot available
KeywordsStressorPsychologyMental healthContext (archaeology)Qualitative researchFocus groupStress (linguistics)Interpersonal communicationMedical educationStress managementVariety (cybernetics)Applied psychologyClinical psychologyMedicineSocial psychologySociology

Abstract

fetched live from OpenAlex

PURPOSE: Excessive stress among post-secondary students has been routinely linked to negative academic and mental health outcomes. The purpose of this qualitative descriptive study was to invite students to identify salient sources of stress within the post-secondary setting in order to facilitate improved measurement of student stress moving forward.METHODS: Focus group interviews were conducted with students from a variety of levels and areas of study. Data was thematically coded into major themes and sub themes, with direct quotes extracted for support.RESULTS: Five major themes of stress were identified, including academics, the learning environment, campus culture, interpersonal, and personal stressors, revealing a multidimensional concept of student stress.CONCLUSIONS: Underlying challenges were revealed, included time management, fear of failure, mental health literacy and education among staff and faculty, and campus inclusivity. The implications of these findings are discussed in the context of existing literature and directions for future research are identified.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0060.004
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.161
GPT teacher head0.518
Teacher spread0.357 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations3
Published2020
Admission routes1
Has abstractyes

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