Effectiveness of a chronic disease self-management program in Mexico: A randomized controlled study
Bibliographic record
Abstract
Objective: To assess the effectiveness of a Spanish-language version of the Stanford Chronic Disease Self-management Program among adults who received medical care in community health centers in Mexico.Methods: This was a prospective, randomized study with Mexican users of community health centers in Tampico, Mexico, conducted between September 2015 and July 2016. A total of 120 adults aged 18 years or older were randomly assigned to intervention (n = 62) and control (n = 58) groups. Data were collected at baseline and at 3 and 6 months post intervention using a structured questionnaire. A repeated measures ANOVA was used for data analysis.Results: Statistically significant differences were found in intervention participants at 3 and 6 months post intervention compared to baseline and the control group for self-management behaviors, including: social activity limitation, quality of life perception, depression, stress, physical activity, communication with physicians, adherence to physician visits, and self-management behaviors.Conclusions: Chronic disease self-management programs (CDSMP) with Mexican adults in community settings are effective in improving their health and self-management behaviors. Further research is needed to assess CDSMP in Mexico and Latin America using objective measurements and examining health outcomes and self-management maintenance over longer periods of time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".