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Record W4283763546 · doi:10.3399/bjgp22x720149

Primary care in Ukraine: an international fellowship perspective

2022· article· en· W4283763546 on OpenAlexaboutno aff
Orest Mulka, Philip Evans

Bibliographic record

VenueBritish Journal of General Practice · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsPrimary careMedicinePerspective (graphical)Primary health careData scienceMedical educationFamily medicineComputer scienceEnvironmental healthArtificial intelligence

Abstract

fetched live from OpenAlex

In the early 1990s after the fall of the Berlin Wall many Central and Eastern European countries, including Russia, sought to review the funding and organisation of health care.Given the appetite for reform, they turned to many different sources for advice; the World Health Organization, other countries in Europe, the US, and Canada.There was a particular interest in reforming primary health care.Ukraine became independent of the old Soviet Union in 1991.At that time its primary healthcare system was based on that of the USSR.Primary care practitioners worked in large polyclinics, based in cities.Their training was limited and their work very circumscribed.They were poorly paid and could be disciplined for failing to refer patients to a specialist at the same polyclinic if there had been an unfavourable outcome from the GP's management.They were not able to treat patients with paediatric or gynaecological problems and, as a whole, the system was very bureaucratic with little personal responsibility for individual patients.This encouraged a high referral rate, which reduced the risks of mistakes and minimised the workload, providing more time for second jobs.These problems had been recognised in Ukraine before independence.In Lviv, Western Ukraine, starting in 1988, progress was made by setting up training schemes for GPs/family doctors -due more to the skill and interest of forward-thinking individuals than any central organisation.In 1990, Ukraine had a low and falling life-expectancy rate (65.6 years for males) and high levels of preventable conditions such as infectious diseases -indicating an inadequate healthcare system.Between 1990 and 1993 the British/ Ukraine Medical Association developed a series of contacts with the Ukrainian Ministry of Health and individual doctors in the country.As a result, help was sought from the Royal College of General Practitioners (RCGP) to move to a system of family doctor-based care, with similarities to the UK system.It was ironic that some

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.022
GPT teacher head0.269
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2022
Admission routes1
Has abstractyes

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