Assessing Motivational Interviewing through Co-Active Life Coaching Tools as a Smoking Cessation Intervention: A Demonstration Study
Bibliographic record
Abstract
The objective of this study was to explore smoking triggers and obstacles to cessation, and intervention experiences among nine 19-28 year old smokers who participated in a 3-month coaching-administered Motivational Interviewing (MI) intervention. In addition to qualitative methods, quantitative trends regarding self-efficacy, self-esteem, cigarette dependency, and average daily cigarettes use were assessed via a repeated measures design. Participants engaged in 9 sessions with a certified coach over 3-months. In-depth interviews and previously validated quantitative assessments were conducted at baseline, 1, 3, and 6-months. Qualitatively, stress and social situations were primary smoking triggers. Cessation obstacles were a sense of personal identify as a smoker and feeling controlled by cigarettes. Through the intervention participants reportedly gained: personal insights related and unrelated to smoking; helpful ways to cope with smoking challenges; and heightened awareness about other choices. Quantitatively, all constructs’ trends supported qualitative findings. The application of motivational interviewing using coaching tools is valuable for reducing smoking, and for providing smokers’ with insights about their behaviours, their triggers, and what they need to be and stay smoke-free. Additional research with a larger sample over a longer time is warranted.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".