Implementing Package of Essential Non-communicable Disease Interventions in the Republic of Moldova-a feasibility study
Bibliographic record
Abstract
Background The aim of this study is to determine the feasibility of implementing and evaluating the World Health Organization Package of Essential Non-communicable Disease Interventions (WHO PEN) approach in primary healthcare in the Republic of Moldova. Methods According to our published a priori methods, 20 primary care clinics were randomized to 10 intervention and 10 control clinics. The intervention consisted of implementation of adapted WHO PEN guidelines and structured training for health workers; the control clinics continued with usual care. Data were gathered from paper-based patient records in July 2017 and August 2018 resulting in a total of 1174 and 995 patients in intervention and control clinics at baseline and 1329 and 1256 at follow-up. Pre-defined indicators describing assessment of risk factors and total cardiovascular risk, prescribing medications and treatment outcomes were calculated. Differences between baseline and follow-up as well as between intervention and control clinics were calculated using logistic and linear regression models and by assessing interaction effects. Results Improvements were seen in recording smoking status, activity to measure HbA1c among diabetes patients and achieving control in hypertension treatment. Improvement was also seen in identification of patients with hypertension or diabetes. Less improvement or even deterioration was seen in assessing total risk or prescribing statins for high-risk patients. Conclusions It is feasible to evaluate the quality and management of patients with non-communicable diseases in low-resource settings from routine data. Modest improvements in risk factor identification and management can be achieved in a relatively short period 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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".