The quantitative assessment of Motivational Interviewing using Co-active Life Coaching Skills as an intervention for adults struggling with obesity
Bibliographic record
Abstract
Objectives:The purpose of this study was to explore Motivational Interviewing (MI) applied through Co-Active Life Coaching (CALC) skills on obese adults’ (ages 35 to 55) weight, waist circumference, self-esteem, functional health status, quality of life, self-efficacy, physical activity, and nutrition. Design:A single-subject multiple-baseline method research design was utilised. Method:One volunteer Certified Professional Co-Active (CPCC) coach provided 18 35-minute weekly coaching sessions with eight women residing in London, Ontario whose BMI was ≥30. Measures included weight, waist circumference, the Rosenberg Self-Esteem Scale, Short-Form 36 (SF-36) Health Survey, the World Health Organization Quality of Life Questionnaire, self-efficacy questionnaires, The International Physical Activity Questionnaire, and two three-day dietary intakes. Participants returned six months after their final coaching session for a follow-up weigh-in and waist circumference measurement. Visual inspection was used to analyse weight and waist circumference to determine whether changes were observed. Statistical interpretations were used to analyse the remaining measures to determine whether a clinically significant change was made. Results:Visual inspection indicated a change in weight and waist circumference. Clinically significant changes were observed in participants’ self-esteem, functional health status, quality of life, self-efficacy, physical activity, and nutrition. At the six-month follow-up, three participants had gained weight (although two participants were still below their baseline weight), one participant continued to lose weight and four participants maintained the weight lost during the intervention phase. Conclusions:MI using CALC skills is a behavioural intervention that is an effective tool in aiding individuals to conquer their battle with weight.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".