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Record W2955286430 · doi:10.1080/08927936.2019.1621524

Does Viewing a Picture of a Pet During a Mental Arithmetic Task Lower Stress Levels?

2019· article· en· W2955286430 on OpenAlexaff
Natalie Ein, M. Said Al Hadad, Maureen J. Reed, Kristin S. Vickers

Bibliographic record

VenueAnthrozoös · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStressorPsychologyTask (project management)Mental arithmeticStress (linguistics)Cognitive psychologySocial psychologyClinical psychologyMedicine

Abstract

fetched live from OpenAlex

Pets can reduce stress in their owner; however, they are not always permitted in public and institutional places. This study examined the impact of people viewing a picture of their pet versus other images on stress levels. One hundred and twenty participants were randomly assigned to one of six conditions. These involved completing a mental arithmetic task while viewing a picture of either their personal pet; an unfamiliar animal; a familiar, supportive person; a stranger; a pleasant image of nature; or no image. Stress was measured through subjective and physiological methods. For participants, viewing a picture of their pet did not reduce their stress response to the task, while viewing a picture of a familiar, supportive person increased the stress response, relative to the controls. Post-stressor, participants in the personal-pet condition rated the picture as making them feel more relaxed, compared with the other conditions. Active interaction with a pet may be required to reduce stress.

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: Bench or experimental · 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
Metaresearch0.0000.002
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 integrity0.0000.000
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 head0.011
GPT teacher head0.314
Teacher spread0.303 · 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 designBench or experimental
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

Citations11
Published2019
Admission routes1
Has abstractyes

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