Can health indicators and psychosocial characteristics predict attrition in youths with overweight and obesity seeking ambulatory treatment? Data from a retrospective longitudinal study in a paediatric clinic in Luxembourg
Bibliographic record
Abstract
OBJECTIVES: The current study aimed to identify factors that could predict attrition in youths starting ambulatory treatment to control or lose weight. DESIGN: Retrospective longitudinal study. SETTING: Paediatric clinic: ambulatory treatment programme. PATIENTS AND MEASURES: A youth sample (n=191; 89 boys; aged 7-17 years) completed measures of demographic characteristics, and health and psychosocial traits before starting an ambulatory weight management programme. Anthropometric and biological markers related to obesity were also obtained. Tests of mean differences and regression analyses were used to investigate the relationship between these variables and attrition after 1 year. RESULTS: and t test results showed both psychosocial and health indicators differentiated between participants who continued attending the treatment programme and those who dropped out. More specifically, youths that dropped out of treatment were significantly older, had higher body mass index z scores, higher levels of insulin, triglycerides and HOMA-IR, reported poorer health, had more conduct problems and were more dissatisfied with themselves and their bodies before starting treatment. Results of regression analyses revealed that weight status (anthropometric and biological markers), age and body dissatisfaction predicted attrition (overall prediction success 73%; prediction success for continued attendance 90/91%; prediction success for dropouts 42/44%). CONCLUSION: Attrition, but especially the continued attendance in treatment, can be successfully predicted by age, weight status and body dissatisfaction. For patients who present with one or more risk factors, careful consideration is needed to decide which (combination of) inpatient or outpatient programme may facilitate prolonged engagement of the patient and hence may be most effective in establishing weight loss.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".