The application and reporting of motivational interviewing in managing adolescent obesity: A scoping review and stakeholder consultation
Bibliographic record
Abstract
Motivational interviewing (MI) is an evidence-based counseling approach that can help individuals make positive behavioral and cognitive changes for managing obesity. We conducted a scoping review to summarize evidence on fidelity and key elements of MI-based interventions for managing adolescent obesity and examine the reporting of these interventions. Ten electronic databases and gray literature were searched systematically and included literature from January 1983 to February 2022, and 26 studies were included. Data on MI features, delivery context, training, and fidelity to treatment were summarized. Fidelity was assessed using an assessment grid with five domains-theory, training, implementation, treatment receipt, and treatment enactment. The last step of the review involved stakeholder consultation with clinician-scientists and researchers with experience in MI and managing adolescent obesity. Thirteen stakeholders were interviewed about our review findings on MI and treatment fidelity. Our analyses revealed that MI-based interventions for managing adolescent obesity had "low treatment fidelity"; no studies had "high treatment fidelity" across all five domains. Fidelity strategies adhered to the most was theory, and treatment enactment was the lowest. Stakeholders mentioned that "low treatment fidelity" may be due to increased time to complete fidelity assessments and increased cost associated with treatment fidelity. These findings have implications for planning, implementing, and evaluating MI-based interventions for managing adolescent obesity.
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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 | 0.101 | 0.217 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".