Bibliographic record
Abstract
Introduction Low-cost computing power has made possible the storage and analysis of large quantities of health data. Individual electronic health records (EHRs) offer the potential for major improvements both in patient care and in the extent and quality of data available for population health research. The Canadian government has committed more than $1 billion in infrastructure funds for the so-called Canada Health Infoway, with a specific mandate to accelerate the development of EHR systems across Canada. Several provincial governments are actively supporting complementary initiatives. (1) In addition to facilitating secondary uses of data that were gathered in the course of providing and financing health care, the 'information revolution' facilitates multiple, low-cost analyses of data gathered for purposes. The proposed Canadian Lifelong Health Initiative (CLHI) relies on this capability. This longitudinal prospective program would involve both an early birth cohort and an aging cohort. Health status information, information related to various social determinants of health, and biological materials would be collected and maintained as a research platform to support multiple individual studies, over a period of decades. (2) A larger scale study in the UK (Biobank) will collect baseline data and blood samples from 500,000 people aged 45-69 years, who will be followed for at least 10 years. (3) Such projects offer unprecedented opportunities to study interactions between genetic variations and environmental variables, which can and should be defined in the broadest sense, and to provide an evidence base for policies and interventions to improve population health. Somewhat belatedly, legislative and policy attention has focussed on the ethical and social implications of these new technological capabilities. In the United States, the main national response has been a health information privacy rule under the Health Insurance Portability and Accountability Act (HIPAA) of 1996. (4) In Canada, legislation governing commercial users of health information and federally regulated industries--the Protection of Personal Information and Electronic Documents Act (5)--which came fully into force in January 2004, complementing a variety of existing federal and provincial privacy statutes. (6) However, this article is concerned not with the content and implementation of these requirements, but rather with a key omission in the underlying ethical and public policy rationale. Privacy, and Beyond At least in the Anglo-American countries, ethical analysis of involving health data is highly individualistic. It tends to focus on potential encroachments on individual privacy; on preventing unauthorized or inappropriate access to data related to the health status of a particular, identifiable individual; and (when data are gathered specifically for purposes) on the nature of the process by which consent is obtained from participants. (7) If confidentiality can be assured, then many ethical problems are considered solved, even if data were originally collected with no indication that they would subsequently be used for purposes. For instance, the Tri-Council Policy Statement (TCPS) that provides guidelines for ethics review of federally funded in Canada states that secondary use of data becomes of concern only when data can be linked to individuals. (8) Commentators often presume that a legitimate tradeoff exists between individuals' wishes to control use of information they have provided and society-wide benefits in the form of improved health system performance or improved understanding of the determinants of health. (9) When data are gathered specifically for research, ethical debate tends to focus on the question of whether and how the process of obtaining consent can take into account uses of data that might not be envisioned at the time the participant is originally recruited, and the circumstances under which renewed or amended consent should be sought. …
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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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.026 | 0.055 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".