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Record W4311681006 · doi:10.22215/etd/2022-15234

The Relationship Between Autonomous Motivation for Exercise, Self-Compassion and Physical Activity

2022· dissertation· en· W4311681006 on OpenAlexafffund
Mackenzie Johnston

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsCarleton University
FundersCarleton University
KeywordsSelf-compassionPsychologyPhysical activityCompassionIntervention (counseling)Test (biology)Social psychologyClinical psychologyMindfulnessPhysical therapyMedicine

Abstract

fetched live from OpenAlex

This series of studies investigated the relationships between self-compassion, physical activity, and autonomous motivation for exercise.The purpose of Study 1 was to examine if autonomous motivation for exercise moderates the association between self-compassion and physical activity in Carleton University students using a cross-sectional design.The goal of Study 2 was to test the effects of a 5-day self-compassion writing intervention on autonomous motivation and physical activity in first year university students at Carleton University.Autonomous motivation did not moderate the relationship between self-compassion and physical activity in Study 1 (B = -1.04,SE = 1.88, t(383) = -0.56,p = .57,95% CI = [-4.75,2.65]).The self-compassion intervention in Study 2 did not affect autonomous motivation, self-compassion, or physical activity across time (p < .05,η 𝑝 2 = .01-.24).Researchers should replicate these findings with larger sample sizes and better measures of self-compassion and physical activity.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.061
GPT teacher head0.378
Teacher spread0.317 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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