Evidence for Health III: Making evidence-informed decisions that integrate values and context
Bibliographic record
Abstract
Making evidence-informed decisions with the aim of improving the health of individuals or populations can be facilitated by using a systematic approach. While a number of algorithms already exist, and while there is no single 'right' way of summarizing or ordering the various elements that should be involved in making such health-related decisions, an algorithm is presented here that lays out many of the key issues that should be considered, and which adds a special emphasis on balancing the values of individual patients and entire populations, as well as the importance of incorporating contextual considerations. Indeed many different types of evidence and value judgements are needed during the decision-making process to answer a wide range of questions, including (1) What is the priority health problem? (2) What causes this health problem? (3) What are the different strategies or interventions that can be used to address this health problem? (4) Which of these options, as compared to the status quo, has an added benefit that outweighs the harms? (5) Which options would be acceptable to the individuals or populations involved? (6) What are the costs and opportunity costs? (7) Would these options be feasible and sustainable in this specific context? (8) What are the ethical, legal and social implications of choosing one option over another? (9) What do different stakeholders stand to gain or lose from each option? and (10) Taking into account the multiple perspectives and considerations involved, which option is most likely to improve health while minimizing harms? This third and final article in the 'Evidence for Health' series will go through each of the steps in the algorithm in greater detail to promote more evidence-informed decisions that aim to improve health and reduce inequities.
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 | 0.048 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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; 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".