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Record W3119390646 · doi:10.29173/bsuj500

Stress in Post-Secondary: Toward an Understanding of Test-Anxiety, Cognitive Performance, and Brief Mindfulness Meditation

2020· article· en· W3119390646 on OpenAlexaffvenue
Raychel Colangelo, Karyn Audet

Bibliographic record

VenueBehavioural Sciences Undergraduate Journal · 2020
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsDouglas College
Fundersnot available
KeywordsAnxietyMindfulnessMeditationTest anxietyPsychologyCognitionClinical psychologyTest (biology)Psychiatry

Abstract

fetched live from OpenAlex

Premised on cultivating present-moment awareness, mindfulness meditation (MM) programs have been shown to significantly reduce state-anxiety and improve cognitive performance in post-secondary students. With increasing popularity, briefer MM formats have been introduced to post-secondary institutions to combat the rising prevalence of student test-anxiety. However, research examining the efficacy of brief MM on a state-level test-anxiety response and its ability to improve cognitive performance in a testing situation is sparse. The present study examined the immediate effects of brief MM on state test-anxiety and cognitive performance. A sample of 50 undergraduate college students (N = 50) were randomly assigned to a brief MM or a control activity. In the current study, it was hypothesized that there would be lower state test-anxiety levels and higher cognitive performance in the brief MM group than the control group. Results revealed that the brief MM group had greater state test-anxiety reduction than the control group at post-treatment. Consistent with previous work, brief MM, however, did not promote any specific short-term benefits for cognitive performance. Our findings, however, converge with past research to suggest that brief MM may produce immediate, short-term state test-anxiety relief. Immediate anxiety relief may be beneficial for students during stressful academic periods.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.109
GPT teacher head0.342
Teacher spread0.233 · 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.

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

Citations4
Published2020
Admission routes2
Has abstractyes

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