Healthy governance: Economic policy and the health industry model
Bibliographic record
Abstract
Health care is often regarded as a priority, as suggested by the resources allocated to it in many countries. The proportion of GDP spent on health in 1998 was 12.9% in the US, for example, 10.35% in Germany, 9.3% in France and 9.3% in Canada (OECD, 2001). However, it is not always clear what it is about health that is significant; why does health care really matter and, within health care, what determines its impact? One view might be that it is essential to life; without a sufficiently high level of health, individuals cannot fulfil their potential as people. Another view might be, for example, that economic production requires a supply of healthy workers who can carry out appropriately assigned tasks. These approaches have different implications for resource allocation; for amounts allocated to health provision and for the types of health care that are prioritised. Moreover, analysis of the processes by which societies set their priorities raises the possibility that the wishes of entire communities are not being served in particular situations. \nThe aim of this paper is to provide a perspective on what it is about health that is especially significant to the development of economies. We emphasise the democratic governance of the organisations and institutions that influence and make up the health industry, suggesting that this is a fundamental determinant of economic success. An objective is to identify the beginnings of a research agenda that follows from this perspective. \nOur discussion is organised as follows. Section II provides the foundations for analysis by briefly considering the ambit of the health industry. It follows very closely Di Tommaso and Schweitzer (2001). Sections III and IV offer our perspective, drawing heavily on Cowling and Sugden (1998, 1999) and Sugden and Wilson (2002). Section III contemplates why the health industry matters, and Section IV discussed the determinants of its impact. Based on this analysis, Section V indicates some items for a research agenda, focusing in particular on topics that are explicitly relevant to public policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".