Why do families enrol in paediatric weight management? A parental perspective of reasons and facilitators
Bibliographic record
Abstract
BACKGROUND: Few children with obesity who are referred for weight management end up enroled in treatment. Factors enabling enrolment are poorly understood. Our purpose was to explore reasons for and facilitators of enrolment in paediatric weight management from the parental perspective. METHODS: Semi-structured interviews were conducted with parents of 10- to 17-year-olds who were referred to one of four Canadian weight management clinics and enroled in treatment. Interviews were audio-recorded and transcribed verbatim. Manifest/inductive content analysis was used to analyse the data, which included the frequency with which parents referred to reasons for and facilitators of enrolment. RESULTS: In total, 65 parents were interviewed. Most had a child with a BMI ≥95th percentile (n = 59; 91%), were mothers (n = 55; 85%) and had completed some post-secondary education (n = 43; 66%). Reasons for enrolment were related to concerns about the child, recommended care and expected benefits. Most common reasons included weight concern, weight loss expectation, lifestyle improvement, health concern and need for external support. Facilitators concerned the referral initiator, treatment motivation and barrier control. Most common facilitators included the absence of major barriers, parental control over the decision to enrol, referring physicians stressing the need for specialized care and parents' ability to overcome enrolment challenges. CONCLUSIONS: Healthcare providers might optimize enrolment in paediatric weight management by being proactive in referring families, discussing the advantages of the recommended care to meet treatment expectations and providing support to overcome enrolment barriers.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".