We have the recipe, but are we following it? Assessing a diabetes prevention program's motivational interviewing fidelity
Bibliographic record
Abstract
Behaviour change interventions designed for exercise adherence and diet modification can reduce the risk of developing type 2 diabetes among those living with prediabetes. Training coaches to deliver these programs as intended increases treatment fidelity, an important aspect to assess delivery and intervention success. A strategy used to monitor and evaluate coaches’ skills post-training includes recording and coding regular client-coach encounters. Small Steps for Big Changes (SSBC) is a diabetes prevention program where clients meet for six one-on-one sessions with a coach. SSBC coaches are trained to deliver sessions using motivational interviewing (MI) at a client-centered level. The aim of this study was to assess treatment fidelity of MI skills for SSBC coaches through the use of the Motivational Interviewing Competency Assessment (MICA) tool. One session per client was randomly selected to be coded using the MICA. Preliminary results for nine SSBC coach-client sessions demonstrated a mean MICA score of 4.23/10 (± 1.25) with a score of 6/10 representing client-centered delivery. These results reflect a generally inconsistent use of MI. Presence of intentions and elements of MI were noted. On average, a client-centered approach was not represented in these findings. Strategies to increase coaches’ level of MI may include offering regular booster sessions and creating a community of practice for coaches to ask questions and improve their skills. Future research within SSBC may look to examine if differences in MI levels, used in delivery, influence client outcomes for exercise and diet adherence and type 2 diabetes risk reduction.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".