Providers’ Perspectives on Electronic Data- Sharing with Patients: A Qualitative Descriptive Study
Bibliographic record
Abstract
IntroductionPatients and healthcare providers routinely share data in the form of personal health information or test results as part of usual care.Traditionally, most data sharing has occurred in-person.However, the technological landscape for sharing, integrating, and analyzing data has constantly evolved and escalated since the onset of .The increased mobilization of technology to support virtual care during the pandemic has the potential to both enhance or limit patient-provider data sharing, but research is limited as to the nature of data sharing and how this may have changed.Several studies examined mental/behavioral health professionals' views on patients' data sharing with providers [2-4].Concerns were identified around privacy, stigma, fear of disclosure, trust, and motivations for care seeking (e.g., prescription refill).Similarly, studies on health-data sharing explored issues of privacy and trust when data are shared outside the patient-provider relationship; for example, provider sharing of data with researchers, insurance companies, or government [5][6][7].A recent scoping review found that sharing patient-generated data collected outside of clinical settings fostered patient-provider communication and improved providers' understanding of their patient's health.However, while patients wanted their providers to be interested and involved in responding to these data, providers had varied interest and were
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".