Barriers and Facilitators of Online Patient Portals to Personal Health Records Among Persons Living With HIV: Formative Research
Bibliographic record
Abstract
BACKGROUND: Federal meaningful use standards are promoting adoption of online portals to personal health records (PHRs). However, relatively little is known regarding barriers and facilitators for vulnerable groups such as persons living with human immunodeficiency virus (PLWH). OBJECTIVE: The objective of this study was to assess barriers and facilitators to use of online PHRs among PLWH. METHODS: We conducted formative research using a written waiting room survey among 120 PLWH regarding barriers and facilitators of portal PHR use. We supplemented findings with data collected from a PLWH focus group, where some members had personal experience with use of a portal. RESULTS: The survey had 90 respondents. Eight PLWH participated in the focus group. Most patients (77/90, 86%) reported having at least some experience using the Internet and most expressed interest in features offered by the portal. Notably, 70% (63/90) expressed some interest in being taught how to use it to communicate with their provider. Focus group themes reinforced these findings, but also voiced concern regarding access to private computers. CONCLUSIONS: Many PLWH in our sample have experience using computers and most are interested in PHR features. However, computer or broadband access and privacy are important barriers.
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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 | 0.061 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".