The views and expectations of young healthy adults about using an online personal health record
Bibliographic record
Abstract
BACKGROUND: Personal health records (PHRs) are tools that allow individuals to access, share and manage their health information online. PHRs have received considerable interest and investment in recent years, because they are thought to have the potential to promote patient self-management and greater involvement in their own care. Despite apparent public interest and a proliferation of available options, adoption rates remain low. There is a gap in our understanding as to why people are choosing to use or not use PHRs, and what different populations of users might want from such a tool. In particular, young adults could be a potential group to target with preventative strategies using a PHR, since technology is much more integrated in their everyday life.OBJECTIVE: This qualitative descriptive study therefore aimed to explore the views and expectations of young healthy adults about using an online personal health record. METHODS: Four focus groups were conducted with a total of 29 participants (18-34 years old) from a community setting in Montreal, Canada. Interviews were transcribed and analyzed with inductive thematic analysis. RESULTS: With respect to how young adults viewed PHRs, three broad themes were identified: perceived advantages to using a PHR; future PHR users; and concerns about PHRs. In terms of what they themselves expected from using a PHR, different themes were elaborated: characteristics of an “ideal” PHR; using the PHR for preventative health; taking more control over their health; and making the PHR worthwhile. A conceptual framework of factors influencing expectations of PHR use in this population is proposed.CONCLUSION: The findings suggest that what young adults perceive as benefits of a PHR may not be the same things that motivate them to actually use a PHR. The results emphasize the fact that more research is needed to understand the expectations and anticipated use of different populations in designing a patient-centered tool. The proposed framework can be used as a basis and tested in future research on PHR adoption.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".