Family physicians' perspectives on personal health records: qualitative study.
Bibliographic record
Abstract
OBJECTIVE: To explore FPs' perspectives on the value of personal health records (PHRs) in primary care and the implementation and adoption of PHRs in Canada. DESIGN: A qualitative design using semistructured interviews. SETTING: Southwestern Ontario. PARTICIPANTS: Ten FPs. METHODS: The 10 FPs participated in semistructured interviews, which were audiotaped and transcribed verbatim. An iterative approach using immersion and crystallization was employed for analysis. MAIN FINDINGS: Participants were generally positive about PHRs, and were attracted to their portability and potential to engage patients in health care. Their concerns focused on 3 main themes: data management, practice management, and the patient-physician relationship. Subthemes included security, privacy, reliability of data, workload, remuneration, physician obligations, patient misinterpretation of medical information, and electronic communication displacing face-to-face visits. Participants identified 3 key facilitators for adoption of PHR systems: integration with existing electronic health record systems, ease of use without being a burden on either time or money, and offering a demonstrated added value to family practice. CONCLUSION: This study replicates previously published literature about FP concerns and opinions, and it further identifies remuneration as a potential barrier in Canadian fee-for-service payment models. Participants identified 3 key facilitators, which were suggested for implementation and adoption of PHRs, providing a basis for future research and development of these systems for use in Canadian family practice.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".