Evaluation and pilot implementation of essential interventions for the management of hypertension and prevention of cardiovascular diseases in primary health care in the Republic of Tajikistan
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background:</ns4:bold> Non-communicable diseases (NCDs) are the leading cause of death worldwide and are a major burden in Tajikistan. The health system of Tajikistan is still shaped by the country's Soviet legacy and the pace of reform has been slow, with high patient out-of-pocket expenditure. The aim of this study is to determine the feasibility of implementing and evaluating essential interventions for the management of hypertension and prevention of cardiovascular disease in primary health care in Tajikistan. </ns4:p> <ns4:p> <ns4:bold>Methods and analysis:</ns4:bold> A pragmatic, sequential mixed methods explanatory design, composed of quantitative and qualitative strands will be used with greater weighting of the quantitative strand. A single geographic district was nominated by the Ministry of Health and chosen for implementation. All primary health care centres in the district that meet inclusion criteria will be included; half will be randomly assigned to the intervention arm and half to the control arm. The overall process is organized into seven steps: (1) refresh clinical decision-making tools including open source WHO PEN and HEARTS resources; (2) update training package for primary health care workers; (3) collection of baseline data; (4) training staff in intervention clinics; (5) implementation of protocols and implementation coaching; (6) collection of follow-up data after 12 months; (7) evaluation of results and sharing experience. </ns4:p> <ns4:p> <ns4:bold>Ethics and dissemination:</ns4:bold> Ethical review and approval have been obtained. Findings will be disseminated at the participant level, national level through a national conference of key stakeholders, and internationally through publication in an open-access peer review journal. </ns4:p>
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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 | 0.007 | 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 | 0.000 | 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".