Atención primaria y responsabilidades de salud pública en seis países de Europa y América del Norte: un estudio piloto
Bibliographic record
Abstract
BACKGROUND: Rapidly occurring changes within the health care systems are creating an opportunity to re-orient the relationships between their different sectors. In order to know the locus of responsibility for various types of preventive activities, we undertook an inquiry on eight areas in six countries from Europe and North America. METHODS: An inquiry among experts based on a matrix which arrayed the type of preventive health services against the target population. Eight clinical conditions were identified (childhood immunizations; adult influenza vaccination; mammography screening, tuberculosis screening, hypertension screening. PKU screening, HIV screening, and osteoporosis testing) trying to know their target population and the locus of responsibility for setting of policy, level to contact individuals for testing, follow-up of people with abnormal tests and maintenance of their medical records. RESULTS: This pilot study showed very little results coincidence either within the eight surveyed areas or across them. There was no regular pattern for the preventive activities studied among the different countries, neither according to the type of health system, nor to the primary health care orientation of the different systems. CONCLUSIONS: There was a limited consensus in the activities studied concerning the best mode of doing public health interventions for personal health services.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".