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Record W2013051066 · doi:10.1080/08964289.2015.1017549

Cue Consistency Associated with Physical Activity Automaticity and Behavior

2015· article· en· W2013051066 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBehavioral Medicine · 2015
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAutomaticityMoodPsychologyHabitPhysical activityConsistency (knowledge bases)Affect (linguistics)Developmental psychologyCognitive psychologyCognitionClinical psychologySocial psychologyMedicineCommunicationNeurosciencePhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

Physical activity is partly regulated by automatic processes such as habits (ie, well-learned responses to cues), but it remains unclear what cues trigger these processes. This study examined the relations of physical activity automaticity and behavior with the consistency of people, activity, routine, location, time, and mood cues present upon initiation of physical activity behavior. Australian adults (N = 1,244, 627 female, M age = 55 years) reported their physical activity automaticity, behavior, and the degree of consistency of these cues each time they start a physical activity behavior. Multiple regression models, which accounted for gender and age, revealed that more consistent routine and mood cues were linked to more physical activity automaticity; whereas more consistent time and people cues were linked to more physical activity behavior. Interventions may more effectively translate into long-lasting physical activity habits if they draw people's attention to the salient cues of time, people, routine, and mood.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.358
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.194
GPT teacher head0.462
Teacher spread0.269 · 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