CRITERIAS FOR ASSESSING THE QUALITY OF MEDICAL SERVICES PROVIDED BY MULTIDISCIPLINARY TEAMS IN THE COUNTRIES OF THE WORLD (LITERATURE REVIEW)
Bibliographic record
Abstract
The article presents an analysis of international experience in assessing the quality of medical care provided to the population through multidisciplinary teams. The main tools for assessing the quality of medical care at the primary level of health care are considered. As tools were considered models of self-evaluation of professional activity of multidisciplinary teams, the level of satisfaction of patients with the medical services provided to them, criteria included in programs for monitoring the work of team specialists. The article reflects the results of the use of quality assessment tools in primary health care organizations in a number of countries: Great Britain, Spain, Canada, China, etc. The results of foreign studies underline the importance of a multidisciplinary approach in health care that affects various aspects of quality of medical care. Assessing criteria such as patient survival, access to care, safety of health services, compliance with recommendations and patient satisfaction allows key factors to be identified, improving the delivery of health care to the public through multidisciplinary teams.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".