The readiness and motivation interview for families (RMI-Family) managing pediatric obesity: study protocol
Bibliographic record
Abstract
Abstract Background Experts recommend that clinicians assess motivational factors before initiating care for pediatric obesity. Currently, there are no well-established clinical tools available for assessing motivation in youth with obesity or their families. This represents an important gap in knowledge since motivation-related information may shed light on which patients might fail to complete treatment programs. Our study was designed to evaluate the measurement properties and utility of the Readiness and Motivational Interview for Families (RMI-Family), a structured interview that utilizes a motivational interviewing approach to (i) assess motivational factors in youth and their parents, and (ii) examine the degree to which motivation and motivation-related concordance between youth and parents are related to making changes to lifestyle habits for managing obesity in youth. Methods From 2016 to 2020, this prospective study will include youth with obesity (body mass index [BMI] ≥97th percentile; 13–17 years old; n = 250) and their parents (n = 250). The study will be conducted at two primary-level, multidisciplinary obesity management clinics based at children’s hospitals in Alberta, Canada. Participants will be recruited and enrolled after referral to these clinics, but prior to initiating clinical care. Each youth and their parent will complete the RMI-Family (~1.5 h) at baseline, and 6- and 12-months post-baseline. Individual (i.e., youth or parent) and family-level (i.e., across youth and parent) responses to interview questions will be scored, as will aspects of interview administration (e.g., fidelity to motivational interviewing tenets). The RMI-Family will also be examined for test-retest reliability. Youth data collected at each time point will include demography, anthropometry, lifestyle habits, psychosocial functioning, and health services utilization. Cross-sectional and longitudinal associations between individual and family-level interview scores on the RMI-Family and these clinical measures will be examined. Discussion As a measurement tool drawing on family-centered care and motivational interviewing, the RMI-Family was designed to increase understanding of the role of motivational factors in pediatric obesity management, allowing healthcare providers and policymakers to manage pediatric obesity more effectively and efficiently. Findings will help to create an innovative, tailored model of health care delivery that uses resources judiciously and is designed to best meet families’ needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".