BEHAVIORAL RISK FACTOR SURVEILLANCE SYSTEM DEVELOPMENT IN THE REPUBLIC OF MOLDOVA
Bibliographic record
Abstract
In the Republic of Moldova cardiovascular diseases are considered to be one of the most important public health issues, showing about 56% of the total mortality structure in the last decade.According to the World Health Organization behavior risk factor is considered as main target of intervention in prevention and control of noncommunicable diseases, including cardiovascular diseases.Despite this fact, an ongoing monitoring of behavioral risk factor is not implemented in the health information system in the Republic of Moldova.The aim of the study was to evaluate the feasibility of a new implementation of the ongoing behavioral risk factor surveillance system in the Republic of Moldova.There was performed many approaches as: SWOT analyses and organizational experiment which results suggested a Delphi survey initiation.There was applied the research design of transversal study using the methodology based on the U.S. behavioral risk surveillance system standards.The systematic random sampling was performed in order to select 800 land telephone numbers.The adults aged 18-69 years were considered eligible for land phone interview provided by the trained staff.As a result of the analysis of data, it has been found that interviewers reached the respondents in 37.5% (95%CI, 34.21% -40.91%) of cases.In conclusion, the low resolution rate is determined by the high level of unresolved cases.The ways to decrease the number of unknown eligibility category is to apply good practices of developed proven ongoing behavioral risk factor surveillance systems of such countries as the U.S.A., Italy and Canada by performing Delphi survey.The consensus on the opinions of experts will contribute to provide more credible evidence based recommendations for a new implementation of the behavioral risk factor surveillance system in the Republic of Moldova.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".