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Record W4301802697 · doi:10.26443/msurj.v8i1.105

Stress reactivity during evaluation by the opposite sex: comparison of responses induced by different psychosocial stress tests

2013· article· en· W4301802697 on OpenAlexaff
Lydia Goff, Nida Ali, Jens C. Pruessner

Bibliographic record

VenueMcGill Science Undergraduate Research Journal · 2013
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsMcGill University
Fundersnot available
KeywordsTrier social stress testStress testStress (linguistics)Task (project management)Fight-or-flight responsePhysiological stressPsychologyTest (biology)Cold pressor testApplied psychologyComputer scienceMedicineEconomicsHeart rateBiologyFinanceManagementInternal medicine

Abstract

fetched live from OpenAlex

It is becoming increasingly difficult for researchers to continue their high rates of publication when funding budgets are running tighter than ever. It is therefore in a researcher’s best interest to utilize more economical tests whenever possible. This project aims to compare various stress tests in order to determine whether the new, cost-efficient Maastricht Acute Stress Test (MAST) activates a physiological and subjective stress response with the same effectiveness as pre-existing, more resource-intensive tests. This study demonstrated that the MAST produces a response similar to that of the previously predominant Trier Social Stress Test (TSST). Meanwhile, database data shows that the purely physiological Cold Pressor Task (CPT) lags behind in terms of response elicited. These findings may allow for a more cost-efficient yet highly effective stress task to become available to researchers.

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)0.002
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.156
GPT teacher head0.512
Teacher spread0.356 · 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

Citations3
Published2013
Admission routes1
Has abstractyes

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