The use of group dynamics strategies to enhance cohesion in a lifestyle intervention program for obese children
Bibliographic record
Abstract
BACKGROUND: Most research pertaining to childhood obesity has assessed the effectiveness of preventative interventions, while relatively little has been done to advance knowledge in the treatment of obesity. Thus, a 4-week family- and group-based intervention utilizing group dynamics strategies designed to increase cohesion was implemented to influence the lifestyles and physical activity levels of obese children. METHODS/DESIGN: This paper provides an overview of the rationale for and implementation of the intervention for obese children and their families. Objectives of the intervention included the modification of health behaviors and cohesion levels through the use of group dynamics strategies. To date, a total of 15 children (7 boys and 8 girls, mean age = 10.5) and their families have completed the intervention (during the month of August 2008). Physiological and psychological outcomes were assessed throughout the 4-week intervention and at 3-, 6-, and 12-month follow-up periods. DISCUSSION: It is believed that the information provided will help researchers and health professionals develop similar obesity treatment interventions through the use of evidence-based group dynamics strategies. There is also a need for continued research in this area, and it is our hope that the Children's Health and Activity Modification Program (C.H.A.M.P.) will provide a strong base from which others may build.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".