A multi-center, randomized, 12-month, parallel-group, feasibility study to assess the acceptability and preliminary impact of family navigation plus usual care versus usual care on attrition in managing pediatric obesity: a study protocol
Bibliographic record
Abstract
BACKGROUND: Pediatric obesity management can be successful, but some families discontinue care prematurely (i.e., attrition), limiting treatment impact. Attrition is often a consequence of barriers and constraints that limit families' access to obesity management. Family Navigation (FN) can improve access, satisfaction with care, and treatment outcomes in diverse areas of healthcare. To help our team prepare for a future effectiveness trial, the objectives of our randomized feasibility study are to (i) explore children's and caregivers' acceptability of FN and (ii) examine attrition, measures of study rigor and conduct, and responses to FN + Usual Care vs Usual Care by collecting clinical, health services, and health economic data. METHODS: In our 2.5-year study, 108 6-17-year-olds with obesity and their caregivers will be randomized (1:1) to FN + Usual Care or Usual Care after they enroll in obesity management clinics in Calgary and Mississauga, Canada. Our Stakeholder Steering Committee and research team will use Experience-Based Co-Design to design and refine our FN intervention to reduce families' barriers to care, maximizing the intervention dose families receive. FN will be delivered by a navigator at each site who will use logistical and relational strategies to enhance access to care, supplementing obesity management. Usual Care will be offered similarly at both clinics, adhering to expert guidelines. At enrollment, families will complete a multidisciplinary assessment, then meet regularly with a multidisciplinary team of clinicians for obesity management. Over 12 months, both FN and Usual Care will be delivered virtually and/or in-person, pandemic permitting. Data will be collected at 0, 3, 6, and 12 months post-baseline. We will explore child and caregiver perceptions of FN acceptability as well as evaluate attrition, recruitment, enrolment, randomization, and protocol integrity against pre-set success thresholds. Data on clinical, health services, and health economic outcomes will be collected using established protocols. Qualitative data analysis will apply thematic analysis; quantitative data analysis will be descriptive. DISCUSSION: Our trial will assess the feasibility of FN to address attrition in managing pediatric obesity. Study data will inform a future effectiveness trial, which will be designed to test whether FN reduces attrition. TRIAL REGISTRATION: This trial was registered prospectively at ClinicalTrials.gov (# NCT05403658 ; first posted: June 3, 2022).
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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 | 0.050 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".