Социологический мониторинг как основа выявления проблем и особенностей повышения квалификации руководителей здравоохранения на региональном уровне
Bibliographic record
Abstract
Aim. To identify the major trends and challenges in improving the skills of healthcare administrators at the regional level, as well as to study the opinions of the heads of medical institutions regarding training of the managerial staff. Methods. The primary materials for the study were the enrollment lists of attendees of postgraduate training, personal cards of the attendees, developed at the Department the electronic form of registration of the conducted training cycles by the years, regulatory and legislative framework of additional professional education, questionnaires. Results. The existing traditional forms of postgraduate training is considered as effective only by 38.9% of the respondents, 44.4% consider them as «more effective than ineffective» and 16.7% very effective. Most of the respondents prefer short period training cycles not exceeding one month, thus for the managers with experience of more than five years relevant are the short-term cycles of intensive training to in the form of «master classes». The vast majority of the respondents believe that the cycles for knowledge improvement should be conducted every five years, and one quarter of respondents every three years. The shortage of staff in the medical institutions, a combination of organizational activities with the job of a clinician dictate the need for the development of distance learning. Compared with the study of 2009 the opinion of leaders on the priority of those cycles of training has changed the most demanded is the information on the legal framework of healthcare. Conclusions. The system of professional education of managers practical health care needs to be further reformed in line with modern conditions of social life.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.016 |
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".