Psychological ownership influences perceived legal ownership of patient medical data
Bibliographic record
Abstract
Abstract We propose beliefs about legal ownership are informed and biased by intuitions about and cues that imbue psychological ownership—knowledge, control, and self-investment. We test our theory in the context of legal ownership of medical data in the United States, which is determined by property rights afforded by HIPAA to three stakeholders: patients, medical providers, and health systems. We theorize that stakeholders underestimate patient rights and overestimate provider rights afforded by HIPAA because patients are perceived to have less knowledge, control, and to have invested less in their data. As predicted, all stakeholders underestimated patient rights and overestimated provider rights afforded by HIPAA in a nationally representative sample of patients ( N =300) and convenience samples of medical providers ( N =114) and health systems administrators ( N =100). All stakeholders agreed that patients should also be afforded more rights by HIPAA and fewer rights should be afforded to health systems. We find additional evidentiary support for our process account in three experiments ( N =1195) in which we manipulated the perceived knowledge, control, and self-investment of patients, which modulated the degree to which patients underestimated rights afforded to them by HIPAA. Our findings illustrate how intuitions about psychological ownership inform and bias judgments of legal ownership, and reveal a consensus of stakeholders agree HIPAA should be reformed to expand patient rights. Public significance This study suggests that beliefs about legal ownership are informed and biased by intuitions about and cues that imbue psychological ownership—knowledge, control, and self-investment. It shows how patients, medical providers and health systems administrators underestimate patient rights and overestimate provider rights afforded by HIPAA, and reveals additional patient rights that all stakeholders agree should be afforded by HIPAA.
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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 | 0.010 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".