To share or not to share – understanding individuals’ willingness to share biomarkers, sensor data, and medical records
Bibliographic record
Abstract
Technological advances in the recent past made it possible for researchers to collect and analyze large amounts of health data at unprecedented scale and speed. For example, fitness trackers and smartwatches produce steady flows of information on individuals’ health. Biomarker data and medical records allow to study individuals at new levels of granularity. The COVID-19 pandemic has highlighted that access to such data for health research and evidence-based public policy decision-making is essential. However, having access to data depends on individuals’ willingness to share their data with others. In this paper, we analyze the factors that may affect the probability of individuals to share their biomarker, health, and sensor data using German survey data and a survey experimental vignette design. We study the impact of data type, recipient, and research purpose on respondents’ willingness to share their data as well as the effects of respondents’ own medical and data sharing history. Overall, participants’ willingness to share biomarker data was higher than the willingness to share other data types. Moreover, those who had shared data before were more willing to do so again. In addition, natural language processing analysis of textual responses capturing respondents’ motives to share their data shows that individuals do understand how valuable their data is for researchers. However, results also underscore that addressing concerns about the protection of data need to be taken seriously. Emphasizing the value of data shared for research and their purpose may help to increase trust and willingness to share data.
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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 | 0.032 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".