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CRITERIAS FOR ASSESSING THE QUALITY OF MEDICAL SERVICES PROVIDED BY MULTIDISCIPLINARY TEAMS IN THE COUNTRIES OF THE WORLD (LITERATURE REVIEW)

2025· review· ru· W4416072582 on OpenAlexaboutno aff
Alyona S. Timofeeva, Н Н Камынина

Bibliographic record

VenueProblems of Social Hygiene Public Health and History of Medicine · 2025
Typereview
Languageru
FieldMedicine
TopicHealthcare Systems and Public Health
Canadian institutionsnot available
Fundersnot available
KeywordsMultidisciplinary approachQuality (philosophy)Health careWork (physics)Public healthPopulationPrimary health careMultidisciplinary team

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.062
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0620.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.441
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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