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Record W4232705221 · doi:10.32920/ryerson.14657019.v1

Does visual exposure to one's pet regulate stress levels during the mental arithmetic task?

2021· preprint· en· W4232705221 on OpenAlexaff
Natalie Ein

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMental arithmeticStressorPsychologyReactivity (psychology)AnxietyTask (project management)Mental imageStress (linguistics)PsychosocialDevelopmental psychologyClinical psychologyCognitive psychologyCognitionMedicinePsychiatryBlood pressureHeart rateInternal medicine

Abstract

fetched live from OpenAlex

This thesis examined the role of viewing a picture of one’s pet as a mechanism for alleviating the symptoms of stress. The mental arithmetic task (MAT), a psychosocial stressor was used to induce stress. Participants were randomly assigned into one of six visual conditions: either a picture of their personal pet (n = 9), an unfamiliar animal (n = 9), a person who is supportive and important to the participant (n = 9), an unfamiliar person to the participant (n =8), a pleasant image (control 1) (n = 8) or no image (control 2) (n = 8). Stress reactivity, both physical (e.g., blood pressure) and subjective (self-reported anxiety), were measured. Findings indicated that contrary to the hypothesis, viewing a picture of one’s personal pet did not reduce stress reactivity, measured either subjectively (self-report) or objectively (physiological assessment). However, the study suggests that various images can influence stress reactivity.

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 score0.002
Threshold uncertainty score0.008

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)0.0020.000

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 head0.019
GPT teacher head0.334
Teacher spread0.315 · 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
Published2021
Admission routes1
Has abstractyes

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Same topicHuman-Animal Interaction StudiesFrench-language works237,207