Understanding and Organizing Health Care Systems
Bibliographic record
Abstract
Abstract As discussed throughout this text, health derives not just from a society’s health care system, but from an array of interlocking political, social, economic, medical, and cultural factors that operate at personal, community, national, and international levels. Moreover, the principal in Nuences on population health, including income, education, production, social welfare programs, tax policy, transportation, and housing, are not a direct part of the health care sector at all. Within the health sector, the elements that most affect health are public health activities such as water supply and sanitation, food inspection, vector insect control, disease surveillance, reduction of industrial pollution, and regulation of pharmaceuticals. Yet most people do not take these societal and public health factors into account when they think about health policy. Instead, they are likely to consider health policy as concerned with the health care system, particularly in terms of clinical or medical care services. Health care policy, then, must be distinguished from a society’s broader health policy: health care services, particularly primary health care, form an important— but far from the sole—component of health policy.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".