Diversity of Pediatric Workforce and Education in 2012 in Europe: A Need for Unifying Concepts or Accepting Enjoyable Differences?
Bibliographic record
Abstract
OBJECTIVE: To evaluate differences in child health care service delivery in Europe based on comparisons across health care systems active in European nations. STUDY DESIGN: A survey involved experts in child health care of 40 national pediatric societies belonging both to European Union and non-European Union member countries. The study investigated which type of health care provider cared for children in 3 different age groups and the pediatric training and education of this workforce. RESULTS: In 24 of 36 countries 70%-100% of children (0-5 years) were cared for by primary care pediatricians. In 12 of 36 of countries, general practitioners (GPs) provided health care to more than 60% of young children. The median percentage of children receiving primary health care by pediatricians was 80% in age group 0-5 years, 50% in age group 6-11, and 25% in children >11 years of age. Postgraduate training in pediatrics ranged from 2 to 6 years. A special primary pediatric care track during general training was offered in 52% of the countries. One-quarter (9/40) of the countries reported a steady state of the numbers of pediatricians, and in one-quarter (11/40) the number of pediatricians was increasing; one-half (20/40) of the countries reported a decreasing number of pediatricians, mostly in those where public health was changing from pediatric to GP systems for primary care. CONCLUSIONS: An assessment on the variations in workforce and pediatric training systems is needed in all European nations, using the best possible evidence to determine the ideal skill mix between pediatricians and GPs.
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 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".