The Effectiveness of the Organization of Work of Medical and Preventive Institutions during the COVID-19 Pandemic: The Patient's View
Bibliographic record
Abstract
The COVID-19 pandemic has presented primary health care organizations with a number of new challenges, including system readiness in terms of infection control, infection prevention, active case finding, and provision of care to patients.The purpose of the study: determination of factors affecting the effectiveness of the primary health care organization based on the results of a sociological survey of respondents to these clinics.Methods. We conducted a one-time prospective cross-sectional study. The study involved 2501 outpatients of polyclinics from 17 regions of the republic at the city and district levels, 2101 relatives of patients of polyclinics, as well as 2133 visitors. The survey was conducted in two stages: during the COVID-19 pandemic in 2022, and in 2023, in the period after quarantine due to the disease.Results. Patients of the clinic trust specialists in 91.2% of cases, regularly visiting them during the period of the disease, during preventive examinations, and dynamic monitoring, but they are not satisfied with long queues for medical appointments (81.7%, (r≤0.001)) and the correctness of patient management (55.2%, (r≤0.001)). In addition, patients believe that a patient's care with one doctor for a long time (more than 5 years) is an indicator of his ability and willingness to work in COVID (r≤0.001) conditions.Conclusions. Summarizing the results of the regression analysis, we were able to predict that proper patient management and counseling of the patient's relatives will reduce the number of visits to several offices by the patient. For patients, it is important to conduct preventive talks and events, as well as the professionalism of the medical staff.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".