Self-assessment health state of adults in Kosovo and Metohia
Bibliographic record
Abstract
Introduction: A self-assessment health condition provides a general approach to creating a picture of the health status of the population. Aim: The aim of the paper is to show the connection between different categories of self-assessed health with socio-demographic characteristics, risk factors and with the presence of one or more chronic non-communicable disease of adults in Serbian communities in Kosovo and Metohia. Method: The research was carried out as a cross section study. As an instrument for data collection, the questionnaire was applied in the 2013 Survey of the Health of the Population of Serbia (excluding Kosovo and Metohija), which is in line with the European Health Research Questionnaire. For the purposes of our research, the following variables were used; gender, age, education, working status, marital status, the presence of chronic non-communicable diseases, smoking, alcohol use and physical activity. Results: A total of 1067 respondents (51.3% of women) responded, with an average age of 42.2 (± 16.0) years. Most respondents in the survey found that they feel very good or good, a quarter of the middle (not bad or good), while their condition was poor or very poor assessed by just under 5% of respondents. Among the respondents who rated their health condition as poor or very poor, there were significantly more female respondents, middle age and 65 years of age. Also, people with primary and secondary education, economically inactive, and who are inclined to the sedentary way of life, have a poor picture of their health. The frequency of people who assess their health status as bad or very bad is the highest among respondents with two or more chronic diseases. Conclusion: Different categories of self-assessment health show a tendency to connect with different individual characteristics of adult respondents. Our results can help in creating a strategy of action and building preventive programs in a defined area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".