Bibliographic record
Abstract
Our first issue of 2007 introduces a redesign of the journal's cover and the page layout for articles, together with a changed referencing system.The new year also offers an opportunity for us to welcome two new members to the editorial board and elicit from our readers further help in developing the journal.The new design replaces the two-column page layout to a single column presentation of papers and moves from the Vancouver to the Harvard system of referencing.We hope these changes make the content of articles more accessible to readers and welcome comments.The two new editorial board members, Stirling Bryan (Birmingham, England) and Michael Shields (Melbourne, Australia), complement the strengths of our existing board with their expertise in economic evaluation and in labour markets and the determinants of health.Economic evaluation is of great importance to the journal and our hope is that we will continue to receive papers that develop the methods of appraisal, which in turn will help improve the techniques of technology assessment and the prioritisation of health expenditure.Although labour typically consumes 60-70% of health care budgets and the corpus of knowledge in labour economics is considerable, the development of this issue in health care has been slow.Current policy reforms, for instance payment for performance (P4P), are social experiments that will, hopefully, be evaluated carefully.Health care reforms designed and implemented inefficiently impose opportunity costs on patients and, like inefficient medical practice, deny patients health care from which they could benefit.In both these areas, economic evaluation and workforce issues, the ubiquitous problem of translating evidence into practice remains.It has been estimated that about 45% of medical interventions are of unknown effectiveness (BMJ Publishing, 2005).Consequently, it is unsurprising that there are well documented and significant variations in clinical practice.However much that is known to improve care is not delivered to patients, thereby imposing avoidable morbidity and mortality.This is so in all countries and the challenge for economists is how can efficiency inducing change be better implemented by redesigned incentives?The effective mitigation of well-recognised policy problems requires the development of greater 'scepticaemia' in the health economics profession.Scepticaemia has been defined by two public health physicians as 'a condition of low infectivity.A Medical School education is likely to confer lifelong immunity' (Skrabanek and McCormick, 1992).We would welcome more contentious and constructive debate in Health Economics and would like to repeat our invitation for readers to submit short editorials (up to 1200 words and 10 references) that not only highlight subject areas and methods suitable for more vigorous development but also critique the status quo and help us all to develop our methods of analysis and improve patient health.
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 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 | 0.046 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.021 |
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; both teacher heads agree on what is shown here.
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".