Adolescent and Caregiver Perspectives on Family Navigation to Improve Healthcare Access and Use for Managing Pediatric Obesity
Bibliographic record
Abstract
We interviewed families to explore their views on the role of family navigation (FN) to improve access to and use of health services for managing pediatric obesity. From March to December, 2020, we conducted individual, structured telephone interviews with adolescents with obesity (13-17 years old) and their caregivers from Edmonton and Calgary, Canada. Among our 37 participants (14 adolescents, 23 caregivers), most (n = 27; 73.0%) reported FN could improve their access to obesity management. Participants recommended several activities to support healthcare access and use, including appointment reminders, evening/weekend appointments, parking/transportation support, and in-clinic childcare, all of which help families to attend appointments over an extended period to support obesity management. Most participants preferred FN be offered by healthcare professional 'navigators' who were approachable, empathic, and compassionate since issues regarding health and obesity can be sensitive, emotional topics to discuss. Overall, families supported integrating FN into multidisciplinary pediatric obesity management to improve healthcare access and use by navigators who apply a range of practical strategies and relational skills to enhance long-term access and adherence to care.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".