REMOTE ASSISTANCE TO PATIENTS WITH CHRONIC NON-COMMUNICABLE DISEASES
Bibliographic record
Abstract
Пандемия COVID-19 показала эффективность дистанционного обслуживания пациентов на уровне первичной медико-санитарной помощи (ПМСП). Эти нововведения требуют поддержки в системе здравоохранения для снижения возрастающей нагрузки на хронические неинфекционные заболевания. Целью нашей работы было выявление заинтересованности пациентов с хроническими заболеваниями в удаленной помощи. По результатам исследования большая часть респондентов готова к онлайн-консультациям, только треть показала нежелание получать онлайн-консультации у врача-27% и медсестры-34%. Установлено, что в период пандемии число больных, состоящих на диспансерном учете, увеличилось по сравнению с предпандемным периодом, число больных, обращающихся за поликлинической помощью один раз в месяц и квартал, и снизилось число больных, обращающихся за помощью один раз в год. В дальнейшем барьерами в дистанционном медицинском обслуживании стали: расширение спектра дистанционных услуг на уровне ПМСП, определение частоты, совершенствование коммуникационных способностей медицинских работников и образование населения о том, что дистанционная медицинская помощь является эффективной альтернативой, необходимость расширения сети возмещения затрат, чтобы медицинские работники могли оказывать дистанционную помощь больным. The COVID-19 pandemic has shown the effectiveness of remote patient care at the primary health care (PHC) level. These innovations require support in the healthcare system to reduce the increasing burden on chronic non-communicable diseases. The purpose of our work was to identify the interest of patients with chronic diseases in remote care. According to the results of the study, most of the respondents are ready for online consultations, only a third showed an unwillingness to receive online consultations from a doctor-27% and a nurse-34%. It was found that during the pandemic, the number of patients registered at the dispensary increased compared to the pre-pandemic period, the number of patients seeking outpatient care once a month and quarter, and the number of patients seeking help once a year decreased. In the future, barriers in remote medical care were: expanding the range of remote services at the PHC level, determining the frequency, improving the communication abilities of medical workers and educating the population that remote medical care is an effective alternative, the need to expand the cost recovery network so that medical workers can provide remote care to patients.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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 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".