Capacity building of health care professionals to perform interprofessional management of non-communicable diseases in primary care – experiences from Ukraine
Bibliographic record
Abstract
BACKGROUND: Non-communicable diseases are leading causes of death and disability across the world. Countries with the highest non-communicable disease (NCD) burden in the WHO European Region are often those that have some of the greatest health system challenges for achieving good outcomes in prevention and care. The aim of this study was to evaluate the effect of an interprofessional capacity building intervention carried out in Ukraine to improve the management non-communicable diseases in primary health care. METHODS: A mixed-methods evaluation study was performed in 2018 to analyse the effect of a capacity building intervention carried out for over 10,000 primary care professionals in Ukraine in 2018. Quantitative data were collected from primary health care records of intervention and control areas preceding the intervention and 1.5 to 2 years after the intervention. Altogether 2798 patient records before and 2795 after the intervention were reviewed. In control areas, 1202 patient records were reviewed. Qualitative data were collected carrying out focus group interviews for health professionals, clinic managers and patients. Also, observations of clinical practice and patient pathways were performed. RESULTS: The capacity building intervention improved the capacity of professionals in detection and management of non-communicable disease risk factors. Significant improvement was seen in detection rates of both behavioural and biological risk factors and in medication prescription rates in the intervention areas. However, almost similar improvement in prescription rates was also observed in control clinics. Improvements in control of blood pressure, blood glucose and cholesterol were not seen during the evaluated implementation period. Qualitative analyses highlighted the improved knowledge and skills but challenges in changing the current practice. CONCLUSIONS: A large scale capacity building intervention improved primary health care professionals' knowledge, skills and clinical practice on NCD risk detection and reduction. We were not able to detect improvements in treatment outcomes - at least within 1.5 to 2 years follow-up. Improvement of treatment outcomes would most likely need more comprehensive systems change.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".