A partnership model to improve population health screening for noncommunicable conditions and their common risk factors, Qazvin, Islamic Republic of Iran
Bibliographic record
Abstract
Early findings and management of health conditions are among the key functions of health care systems. We developed a partnership framework to establish an extended primary health care-based selective screening service for the entire population of a small town as a key project of Qazvin Health Plan, Qazvin, Islamic Republic of Iran. Eight scientific associations and a diverse technical taskforce extensively reviewed evidence to adapt the grade A and B preventive recommendations of the American Preventive Service Taskforce. A list of 15 priority health conditions was identified and screening protocols were developed accordingly. Then strategies for working with private sector providers for better health care were applied to form our partnership model through which we ensured provision of screening services in 3 areas: service provision, quality and costs. Six private medical offices and a laboratory cooperated with the public health centre of the town to screen eligible residents. Preliminary analysis of the results suggests that the framework has successfully engaged private care providers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".