Efficiencies, Risks, and Rabbit Holes: Primary Care Providers' Perceptions of Using Email to Communicate with Patients: Results from the MAAP-NS Census Survey Data (Preprint)
Bibliographic record
Abstract
BACKGROUND Prior to COVID-19, few primary care providers (PCPs) in Canada used virtual care compared to other countries. Despite known benefits for providers and patients, concerns about security and quality of care hindered the uptake of virtual care options. During the COVID-19 pandemic, virtual care options were rolled out rapidly out of necessity to continue to provide primary care while minimizing transmission of the virus. Video and telephone virtual care options have been most widely used, with little attention given to asynchronous modalities such as secure messaging and video. OBJECTIVE As decision-makers consider the role of virtual care moving forward into a post-pandemic world, it may be useful to explore the perspectives of PCPs on the use of email pre-COVID. METHODS We analyzed a subset of data from the MAAP-NS Study follow-up provider survey, looking at two questions from the survey pertaining to the use of email in primary care. RESULTS PCPs generally felt that email would not be useful to correspond with patients in their practice. PCPs identified barriers to email utilization, including lack of fee codes, the potential for increased work, and concerns around patient access and use. PCPs identified some benefits of email use, including flexibility, ease of use, and improved documentation. CONCLUSIONS PCPs identified benefits of email use in practice, but there is a need to address the concerns and barriers identified by providers to improve the uptake of email communication in practice and ensure that it is an innovation that remains beyond the COVID-19 pandemic. Many concerns raised by PCPs can be managed through various policies. Our findings give voice to providers who must choose whether to use this modality and highlights what is needed to make this modality successful. We have identified the core concerns of PCPs and have identified for whom this modality is useful and for whom it is more challenging. These findings are useful for Canadian PCPs, where the use of email has been underexplored. During the COVID-19 pandemic, there has been an increase in the use of email in primary care practice, but the implementation has been largely done informally. Formalizing this modality through practice guidelines, policies, and remuneration could support its success.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".