Bibliographic record
Abstract
Preface The idea for this book came to me when I was teaching a course on epidemiology to graduate students in the Health and Health Policy (HHP) Programme at Princeton University’s Woodrow Wilson School for Public and International Affairs. Many of my students were completing a Master’s degree in Public Administration (MPA) or Public Policy (MPP). They had already worked in government or for well-known international non-governmental organisations (NGOs) and had been involved in making decisions that could affect the health of hundreds of thousands of people. Yet for the most part they did not have any formal education or training in health sciences upon which to base these decisions. With undergraduate degrees in political science, management and economics, the process of producing, appraising and using scientific evidence was a “black box” that was unveiled during the course so that the students could be more critical readers of the research literature (or even of reports of the literature published in the media, which is where most people read about scientific evidence). Even health practitioners working on the frontlines – including doctors, nurses, midwives, lay health workers and others – are not always well versed in research methods and how research findings can be used to improve health. While evidence is certainly not the only “ingredient” that goes into decision-making for health, making decisions without evidence is like sailing the seas without a map and compass. Therefore, to foster more evidence-informed decision-making, I thought it would be important to write a book targeted towards practitioners and policy-makers that demystifies the process of knowledge production and illustrates the complexity of decision-making so that knowledge users are better able to incorporate the scientific evidence into decisions, to thereby influence health outcomes in a more strategic and informed way. This is by no means an epidemiology textbook, but rather a practical guide to evidence-informed decision-making with the goal of improving health and reducing health inequities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.539 | 0.365 |
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".