Why Do Russians Avoid Medical Care? (Socio-Demographic Determinants and Reasons for Avoidance Behavior)
Bibliographic record
Abstract
Refusing to seek necessary medical assistance or avoiding visits to healthcare facilities has serious negative health consequences and imposes a significant healthcare cost burden. There is a limited amount of literature on avoidance behavior in this area. The aim of this study is to examine the prevalence and causes of avoidance behavior among Russian adults; identify factors associated with the avoidance of necessary medical care; and recognize the most disadvantaged socio-demographic groups most often exposed to avoidance behavior in general and according to specific causes. The informational base of the study is the Comprehensive Survey of Living Conditions conducted by Rosstat in 2022. Multivariate logistic regression models have been constructed to assess the determinants underlying avoidance behavior. Avoidance of medical care is measured by asking respondents whether they had situations when they needed a medical examination or consultation with a doctor, but did not seek help from a medical organization. Those who answered affirmatively were asked to choose the reasons for not seeking medical assistance. It was found that nearly a quarter of adult Russians refrain from visiting a doctor, despite being aware of their need for medical care. The individuals who avoid seeking medical assistance are more often women, elderly, in poor health, with secondary or higher education, belonging to a low-income group, employed, living in cities with a population of less than 1 million or in rural areas, and those who consume alcohol or smoke. Among those refusing medical assistance for about one-third the main reasons for such behavior were the unsatisfactory performance of the medical organization or distrust in the effectiveness of treatment. Another fifth and sixth of the avoiders report, respectively, that they do not have time and that treatment can only be obtained on a paid basis. Groups of the population that avoid medical assistance for specific reasons have been identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".