Bibliographic record
Abstract
The Swiss healthcare system is considered to be among the best performing healthcare systems among countries that are members of the Organisation for Economic Co-operation and Development (OECD) (OECD/WHO 2011; Commonwealth Fund 2014). Its citizens are highly satisfied with the health system (Interpharma, n.d.), mainly because of an almost unconstrained freedom of choice and overall large supply of healthcare providers and hospitals. However, a 2011 analysis of the Swiss healthcare system identified fragmented provision of services, along with lack of coordination and integration as major deficits and suggested that the relatively high healthcare expenditures were not being used efficiently. Whether Switzerland “receives value for money for its major financial investment in healthcare” (OECD/WHO 2011) is being questioned. In this section, we focus on one cantonal programme, the “Programme cantonal Diabe`te” (PcD, Hagon-Traub et al. 2010), which was launched in 2010 in the canton of Vaud, a Swiss canton with a population of approximately 720,000 (about 10% of the Swiss population).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.483 | 0.233 |
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".