Families’ perceived benefits of home visits for managing paediatric obesity outweigh the potential costs and barriers
Bibliographic record
Abstract
AIM: Home visits have successfully been used to deliver various health services, but what role could they play in paediatric weight management? Low treatment initiation and high attrition prompted our multidisciplinary paediatric weight management clinic to investigate how families perceived the benefits and barriers of home visits. METHODS: We focused on children with obesity aged 2-17 who were enrolled in our tertiary-level clinic in Alberta, Canada. None had received a home visit. The families were interviewed face-to-face from October 2015 to October 2016, and we used a qualitative description methodological framework and manifest content analysis. The parents were the main interviewees. RESULTS: Of the 56 families, 89% were interested in a home visit, 82% wanted support from a dietician and 54% from an exercise specialist. The perceived benefits of home visits included comprehensive assessment (95%), convenience (86%), tailored care (29%) and family involvement (13%), while the costs and barriers included clinicians' potential judgmental attitudes (30%), loss of privacy (19%) and distractions (10%). Some thought clinicians would find home visits inconvenient (25%), with bureaucratic challenges (14%) and sustainability issues (5%). CONCLUSION: Families felt home visits were a convenient option for managing paediatric obesity and identified important benefits and barriers that could guide such interventions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".