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Record W2747559424 · doi:10.1177/174183051501300208

Assessing the impact of Motivational-Interviewing via Co-active Life Coaching on engagement in physical activity

2015· article· en· W2747559424 on OpenAlexafffund
Don Morrow

Bibliographic record

VenueInternational journal of evidence based coaching and mentoring · 2015
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsWestern University
FundersGoddard Space Flight CenterUniversity of Waterloo
KeywordsCoachingMotivational interviewingPsychologyInterviewPhysical activityApplied psychologyMedicineSociologyPsychotherapistPhysical therapyPsychological intervention

Abstract

fetched live from OpenAlex

The purpose of this 12-week pre-post design study was to assess the impact of Motivational Interviewing via Co-Active Life Coaching (MI-via-CALC) on engagement in physical activity for 25 women between the ages of 30 and 55 years. Data on task self-efficacy, barrier-specific self-efficacy, self-esteem, physical activity (PA), body mass index (BMI), and waist-to-tip ratio and circumference were collected. Results indicated some positive, but not significant differences in barrier-specific self-efficacy, which were detected between pre- and post- intervention, and statistically significant differences in self-esteem between pre- and post-intervention were found. No statistically significant differences were found in participants’ task self-efficacy scores and PA scores. Statistically significant decreases were detected for BMI, and waist-to-hip ratios and circumference. MI-via-CALC is an encouraging approach for women who are seeking a more physically active lifestyle, and additional research with a larger sample size is recommended.

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 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)0.002
metaresearch head score (Gemma)0.002
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 score0.359
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.341
GPT teacher head0.502
Teacher spread0.161 · 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 teacher head, 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

Citations4
Published2015
Admission routes2
Has abstractyes

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