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Record W7115818203

Variation in daily and within day intentions and the intention-behaviour gap

2020· dissertation· en· W7115818203 on OpenAlexaboutno aff

Bibliographic record

VenueMacSphere (McMaster University) · 2020
Typedissertation
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsLogistic regressionPhysical activityScale (ratio)Variation (astronomy)Descriptive statisticsRegression analysisConstruct (python library)Theory of planned behavior
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The construct of intention continues to be an important correlate and predictor of physical activity; however, a substantial intention behaviour gap continues to exist. Little literature has examined this gap on a micro-temporal scale, and none have addressed the adolescent population. PURPOSE: The purpose of this thesis is to 1) examine whether there are variations in daily and within day intentions to be physically active in the adolescent population, and 2) whether the intention -physical activity gap is reduced when assessing intention and behaviour on a micro-temporal scale using Ecological Momentary Assessment (EMA). METHODS: This thesis sample included 193 grade 11 students from a large school board in Southern Ontario. Participants responded to 5 EMA prompts for 7 days on their smartphones and wore accelerometers for the duration of the study. Each EMA prompt included a brief questionnaire assessing participant intentions to engage in physical activity. A mixed-effects logistic regression model was used to determine variability in intentions and descriptive analyses were used to examine the intention - behaviour gap. RESULTS: A mixed-effects logistic regression did not indicate differences in intentions between days of the week (coef. = -0.07 SE: 0.07, p=.27) but did indicate that likelihood of reporting intentions significantly decreases over the course of the day (coef. = -.479 SE=.05, p<.01). For daily intentions and physical activity, 89% of daily intenders engaged in subsequent physical activity while 46% of within day intenders engaged in subsequent physical activity. CONCLUSIONS: Findings suggest that there is some variation in intentions and that a micro-temporal time scale measurement serves to reduce the intention - behaviour gap. This adds to our understanding of the relationship between intentions and physical activity. In better understanding this relationship, we can begin to guide interventions that bridge the gap between intentions and physical activity in the adolescent population.

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)0.003
metaresearch head score (Gemma)0.012
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 score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.035
GPT teacher head0.292
Teacher spread0.257 · 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
Published2020
Admission routes1
Has abstractyes

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