Multilevel Causal Analysis of Socio-Psychological and Behavioral Factors of Health Providers and Clients That Affect Health Behavioral Modification in Obesity
Bibliographic record
Abstract
UNLABELLED: The Comprehensive Lifestyle Intervention, which integrates behavioral therapy, is the main ideal management of the clients with obesity. Various socio-psychological factors can affect outcome of the program. THE PURPOSES: to determine the socio-psychological factors at the client and provider groups that affect health behavior modification (HBM) in obese clients, and to investigate the cross-level interaction of factors that affect HBM. The samples included 87 health providers and 412 clients using stratified random sampling. Hierarchical Linear Model was used to analyze in a questionnaire with reliability of 0.8-0.9. RESULTS: 1) for the clients: 1.1) Attitudes towards healthy behavior (AHB), health-related knowledge, and trust in the provider predicted self-efficacy at 49.40%; 1.2) AHB and support from the provider predicted self-regulation at 75.50%; and 1.3) AHB, trust in the provider and support from the provider predicted self-care at 26.6%. 2) for the health providers: 2.1) Health quotient (HQ), project management (PM), support from the team, and the team emotional quotient (EQ) predicted self-efficacy at 71.30%; 2.2) PM and HQ predicted self-regulation at 51.60%; and 2.3) PM, team EQ and HQ predicted self-care at 77.30%., 3) No cross-level interaction of factors between the clients and the providers was identified to affect HBM. CONCLUSION: the obese client's AHB is the factor that significantly influenced self-efficacy, self-regulation and self-care (3SELF). At the health provider level, both HQ and PM significantly influenced 3SELF. Behavioral.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".