Bibliographic record
Abstract
Extract Foreword to the Third edition A concept is a general idea or understanding, usually of something abstract rather than concrete. Raj Bhopal’s Concepts of Epidemiology conveys the general ideas and understanding of epidemiology supremely well, perhaps better than any other monograph on the discipline of epidemiology. It has the added virtues of being clearly and concisely written, and provides comprehensive cover of all essential aspects. I thought the first edition was the best book of its kind that I had ever read. The second edition was even better; and now this third edition clarifies the ideas and enhances understanding even better than ever. It is a book that ought to be bedside reading for all epidemiologists everywhere. John M. Last Emeritus Professor of Epidemiology University of Ottawa Foreword to the second edition The exciting, innovative features of Raj Bhopal’s book are the emphasis throughout on a conceptual approach and the systematic focus on underlying concepts and fundamental principles. This approach leads students of epidemiology, the intended readers of the book, to a logical understanding of the methods and procedures used in epidemiological research and practice. That was why I endorsed the first edition with unqualified enthusiasm.
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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 0.033 |
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".