Clinical Preventive Services in Guatemala: A Cross-Sectional Survey of Internal Medicine Physicians
Bibliographic record
Abstract
BACKGROUND: Guatemala is currently undergoing an epidemiologic transition. Preventive services are key to reducing the burden of non-communicable diseases, and smoking counseling and cessation are among the most cost-effective and wide-reaching strategies. Internal medicine physicians are fundamental to providing such services, and their knowledge is a cornerstone of non-communicable disease control. METHODS: A national cross-sectional survey was conducted in 2011 to evaluate knowledge of clinical preventive services for non-communicable diseases. Interns, residents, and attending physicians of the internal medicine departments of all teaching hospitals in Guatemala completed a self-administered questionnaire. Participants' responses were contrasted with the Guatemalan Ministry of Health (MoH) prevention guidelines and the US Preventive Services Task Force (USPSTF) recommendations. Analysis compared knowledge of recommendations within and between hospitals. RESULTS: In response to simulated patient scenarios, all services were recommended by more than half of physicians regardless of MoH or USPSTF recommendations. Prioritization was adequate according to the MoH guidelines but not including other potentially effective services (e.g. colorectal cancer and lipid disorder screenings). With the exception of colorectal and prostate cancer screening, less frequently recommended by interns, there was no difference in recommendation rates by level. CONCLUSION: Guatemalan internal medicine physicians' knowledge on preventive services recommendations for non-communicable diseases is limited, and prioritization did not reflect cost-effectiveness. Based on these data we recommend that preventive medicine training be strengthened and development of evidence-based guidelines for low-middle income countries be a priority.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".