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Record W4316669561 · doi:10.31234/osf.io/s4r3n

Affective responses in adults with severe obesity living or not with a mental disorder during two consecutive 6-min walking exercises.

2023· preprint· en· W4316669561 on OpenAlexaff
Louis Pitois, Aurélie Baillot, Benjamin Pageaux, Ahmed Jérôme Romain

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationInstitut Universitaire de Gériatrie de MontréalUniversité du Québec en OutaouaisInstitut du Savoir MontfortUniversité de MontréalInstitut Universitaire en Santé Mentale de Québec
Fundersnot available
KeywordsObesityBody mass indexPsychologyAffect (linguistics)PleasureMedicinePhysical activityClinical psychologyPsychiatryPhysical therapyInternal medicinePsychotherapist

Abstract

fetched live from OpenAlex

Affective response during physical activity can partly determine its adherence in adults with severe obesity living or not with a mental disorder. This study examined affective responses to physical activity in adults with severe obesity living or not with a mental disorder. Two groups (24 adults with severe obesity, body mass index = 44.4 kg/m², SD = 6.5; 20 adults with severe obesity and mental disorders, body mass index = 44.1 kg/m², SD = 9.3) two 6-min walking exercises with affective responses assessed at each minute. No between-group in the distance walked was found. Results showed a significant time effect indicating a decline in pleasure over time in both groups from the first to the second exercise. No interaction effects were detected in both exercises. Affective responses similarly declined in both groups indicating that obesity, rather than the presence of a mental disorder, is partly responsible for this decline.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.021
GPT teacher head0.312
Teacher spread0.291 · 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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