Trends in collection, use and disclosure of personal information in contemporary health research: challenges for research governance.
Bibliographic record
Abstract
Background: Changes in the Nature and Use of Personal Information Health Research Health research encompasses a heterogeneous set of research activities. This paper focuses on challenges that arise in the governance of observational research which is usually carried out without any direct contact between the researcher and the individuals being studied. Two broad areas of health research are heavily dependent on access to a wide range of existing person-level health information: 1. Public health, occupational health and safety, and the non-medical determinants of health and disease. The latter examines the relationship between health and lifestyle, environmental, and socioeconomic factors including income and education. Epidemiology is the foundation of much of this type of research. Research in this domain links both health and non-health information, such as occupation, education, and lifestyle information. 2. Health policy, health services research, and program evaluation examine the health care system and the effects of different policies and methods of health care delivery on the quality and efficiency of care provided. This type of research is informed by a wide variety of disciplines, including: economics, health policy, political sciences, sociology, anthropology, medicine, and epidemiology. Most health research requires person-level data, chiefly to increase precision in analysis. For example, when trying to determine the effect of exposure to an environmental toxin in a neighbourhood, with person-level data one can better examine the causal relationship by controlling for or holding constant known personal factors such as age and sex of the individual that relate to the outcome of interest. Similarly, when evaluating a policy to increase co-payments prescription drugs, it is prudent to examine across different income brackets the impact of that policy on the tendency to discontinue medications. In some cases, if using aggregate rather than individual-level data, it is possible to come to spurious conclusions about the effect of exposure (whether to a policy or an environmental toxin) on health outcomes. (1) Also, individual-level data are required to link information from disparate databases. This linkage creates the ability researchers to answer a much broader set of questions about the determinants of health, but it also raises major privacy concerns when these activities are being conducted without individual consent. Although data may be stripped of direct personal identifiers, the resultant records are often so rich in information that the residual risk of disclosure of identity through indirect means is sufficiently high that the data must be treated as if they were identifiable. In fact, with as little information as date-of-birth, sex, and full postal code, the majority of individuals in a particular region may be re-identified by linking with census tract information. (2) Trends in Data Collection, Use, Storage, and Disclosure Twenty years ago, only a handful of research centres across North America had the capacity to manipulate and link large data sets, and most government and other data repositories were used only claims adjudication. Medical records were all paper-based. Advances in the capacity of computers and the internet to store, manipulate and disseminate large amounts of data have changed dramatically the nature of collection, use and disclosure of personal information in contemporary health research. These advances have spawned two parallel developments: the planning and development of large disseminated health information networks that will serve multiple purposes beyond those direct clinical care; and the proliferation of decentralized holdings of personal data. In Canada, the United States, much of the European Union, Australia, and New Zealand, major efforts are underway to computerize patient records across health care settings, with the ability to share and link information from the records of physicians, diagnostic facilities, and health care institutions. …
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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 | 0.535 | 0.694 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.033 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.027 | 0.036 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier 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".