Prescribing exercise in primary care: Ten practical steps on how to do it
Bibliographic record
Abstract
blogs at bmj.com/blogs Secondly, agreement is needed on how to cost and value the immediate and longer term health benefits of mitigating climate change. The multiple health benefits of reducing carbon emissions and their effect on the economy are yet to be systematically captured and valued. The importance of doing this was recognised in the Stern review.12 Designing and agreeing systematic measures of the health benefits of taking action requires interdisciplinary collaboration between health professionals, economists, and climate change scientists. It also involves interagency collaboration between the Department of Energy and Climate Change; the Department for Environment, Food and Rural Affairs; the Department of Health; and the National Institute for Health and Clinical Excellence to ensure that this is done in rigorous ways that benefit patients, the public, and future generations. These methods need to quantify the multiple benefits in ways that stimulate action from all parts of the global health system, from local nurses and doctors to global drug companies. 1 Costello A, Abbas M, Allen A, Ball S, Bell S, Bellamy R, et al. Managing the health effects of climate change. Lancet 2009;373:1693-733. 2 Jarvis L, Montgomery H, Morisetti N, Gilmore I. Climate change, ill health, and conflict. BMJ 2011;342: dl819. 3 Gilding P. The great disruption: how the climate crisis will transform the global economy. Bloomsbury, 2011. 4 Griffiths J, Rao M. Public health benefits of strategies to reduce greenhouse gas emissions. BMJ 2009;339;b4952. 5 Roberts I, Edwards P. The energy glut: the politics of fatness in an overheating world. Zen Books, 2010. 6 Zander A, NiggebruggeA, Pencheon D, LyratzopoulosG. Changes in travel-related carbon emissions associated with modernization of services for patients with acute myocardial infarction: a case study. J Public Health 2011;33:272-9. 7 Connor A, Mortimer F, Higgins R. The follow-up of renal transplant recipients by telephone consultation: three years experience from a single UK renal unit. Clin Med 2011;11:242-6. 8 NHS Sustainable Development Unit, Forum forthe Future. Fitforthe Future. Scenarios for low-carbon healthcare 2030. Cambridge: 2009. 9 HM Treasury. The green book. 2011.www.hm-treasury.gov.uk/ data_greenbook_index.htm. 10 Atkinson S, Ingham J, Cheshire M, Went S. Defining quality and quality improvement. Clin Med 2010;10:537-9. 11 World Bank. Data. 2009. http://data.worldbank.org/indicator/. 12 Stern N. Stern review on the economics of climate change. 2006. http://webarchive.nationalarchives.gov.uk/+/http:/www.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".