Exposure to and attitudes regarding electronic healthcare (e‐Health) among physician assistants in Canada: A national survey study
Bibliographic record
Abstract
Physician assistants (PAs) are a growing group of healthcare providers who could facilitate the adoption of electronic healthcare (e-Health) into practice. In 2018, we conducted a Canada-wide web-based survey study of practicing PAs and student PAs regarding their current exposure to e-Health, as well as their perceived value for its use and interest in future adoption. For this study, e-Health was defined as technology that allows direct communication between patients and healthcare providers or facilitates patient self-management for the purpose of assessment and management. We focused on telehealth, direct messaging (e.g. text, email), patient-directed web-based applications (apps) and patient-provider shared web-based apps. Survey responses were analysed descriptively and compared between practicing and student PAs with Chi-square tests of independence. We also examined correlations between age, exposure, perceived value and interest in future adoption for practicing PAs and student PAs separately. About 186 respondents completed the survey; 145 practicing PAs and 39 student PAs. Fewer than half of respondents had exposure to the studied e-Health modalities. Compared to practicing PAs, student PAs more often perceived value in e-Health and expressed interest in its expanded adoption. In both groups, perceived value frequently correlated significantly with interest in adoption. Student PAs report little formal education during their training, and both practicing PAs and student PAs note a need for infrastructure support, and general knowledge about what is available and safe in order to enable them to expand their use of e-Health in practice. The most interest is present for patient-directed apps and patient-provider shared apps. With workload and remuneration barriers to physician adoption of e-Health, salary-based PAs could have a role in facilitating the integration of e-Health solutions into practice. Additional awareness, exposure and support for PAs to do so are required.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".