A Multidisciplinary Approach to Health Prevention with the Emphasis on Multimorbidity in Post Conflict Serbia – Results of the Qualitative Research
Bibliographic record
Abstract
In the period 2019-2021, the Serbian team (consisted form representatives of the Institute for Biological Research "Siniša Stanković", Institute of Public Health of Serbia "Dr Milan Jovanović Batut", Environmental Protection Agency, the Medical Faculty in Belgrade, Ministry of Health and the Institute of Social Sciences) conducted the research project on health prevention and multimorbidity in post conflict Serbia. Objective was to understand the perception of relevant actors about possible risk factors (environmental, behavioral, and socio-economic) for the occurrence of multimorbidity. Methodology applied in qualitative research was focus groups and interviews with the sampled population group representatives. Target population was health professionals, health providers’ and local municipalities’ management. The purposes of the research were: 1) insight into the main multimorbidity factors through the prism of stakeholders on the local level; 2) drafting recommendations on changing regulation and practice in public health prevention measures. Results of the research show that there is an agreement in the stakeholder perception that multimorbidity in Serbia is increasing and that preventive measures should be strengthened. All groups of predictors (environmental, behavioral, and socio-economic) are perceived as of equal importance. The research was the basis for development of the legislative and systemic recommendations.
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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 | 0.033 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".