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Can a Simple Distraction Lower Fear Levels During a Sudden Scary Outdoor Situation?

2023· article· en· W4388734669 on OpenAlexaff
Roger T. Couture

Bibliographic record

VenueThe Physical Educator · 2023
Typearticle
Languageen
FieldPsychology
TopicAdventure Sports and Sensation Seeking
Canadian institutionsLaurentian University
Fundersnot available
KeywordsDistractionHeart rateAnxietyPsychologyStressorAnalysis of varianceJumpingMixed-design analysis of varianceAdventureSensation seekingBoredomClinical psychologyAudiologySocial psychologyMedicinePsychiatryPersonalityCognitive psychologyBlood pressureInternal medicinePhysiology

Abstract

fetched live from OpenAlex

Adventure-based teaching can foster social and personal growth yet can scare and cause long-lasting anxiety in some group members. This study examined the effects of a simple distraction to lower stress levels during an approaching scary event. Forty-eight males (M = 20.2 years) were randomly assigned to one of four groups. The stressor involved participants walking on a 3-m tower while blindfolded and jumping off into a pool. Self-report questionnaires, digital counters, heart rate, and peripheral temperature measures were used. A two-way analysis of variance indicated no significant difference in trait and state anxiety/ sensation seeking. However, a post hoc Scheffé’s test found significant changes in heart rate between the experimental groups and the control groups. Though not statistically different, the study’s results suggest that distractions affect heart rate, peripheral temperature, and state anxiety/sensation seeking, with raw scores rising (5% and 18%, respectively, for the latter). Future studies should consider other distraction-type strategies in which stress levels may hamper safety during group adventure activities.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.034
GPT teacher head0.352
Teacher spread0.318 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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