Big Data and Population Health
Bibliographic record
Abstract
We are at the dawn of a data deluge in health that carries extraordinary promise for improving the health of populations. However, current associated efforts, which generally center on the 'precision medicine' agenda, may well fall short in terms of its overall impact. The main challenges, it is argued, are less technical than the following: (1) identifying the data that matter most; (2) ensuring that we make better use of existing data; and (3) extending our efforts from the individual to the population by exploiting new, complex, and sometimes unstructured, data sources. Advances in Epidemiology have shown that policies, features of institutions, characteristics of communities, living and environmental conditions, and social relationships all contribute, together with individual behaviors and factors such as poverty and race, to the production of health. Examples are discussed, leading to recommendations that focus on core priorities for data linkage, including those relating to marginalized populations, better data on socioeconomic status, micro- and macro-environments, collaborating with researchers in the fields of education, environment, and social sciences to ensure the validity and accuracy of multilevel data, aligning research aims with policy decisions that must be made, and heightening efforts to protect privacy.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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".