Electronic communication between family physicians and patients
Bibliographic record
Abstract
OBJECTIVE: To assess the proportion of academic family physicians using e-mail with patients and to explore related attitudes, barriers, and facilitators. DESIGN: A 47-item questionnaire was created after a literature review, discussions with study team members, pretesting, and pilot testing. The questionnaire was disseminated electronically from June to August 2017. SETTING: Ontario. PARTICIPANTS: All family physicians affiliated with the Department of Family and Community Medicine at the University of Toronto. MAIN OUTCOME MEASURES: Physician practices using e-mail (including barriers to and facilitators of e-mail use with patients), use of e-mail with other health care providers, use of communication technologies other than e-mail, and demographic and practice information. RESULTS: A total of 1553 surveys were disseminated and 865 responses received (56% response rate). Overall, 610 respondents met inclusion criteria. Of these respondents, 43% (265 of 610) personally sent e-mails to patients in a typical week. An additional 21% (126 of 610) reported that they did not personally e-mail patients, but their clinic staff did. Patient convenience and a decrease in the need for telephone communication were the most commonly noted reasons for e-mail use. Facilitators of e-mail use included integration with the electronic medical record, enhanced e-mail access control, security features, and financial compensation. Barriers to e-mail use included privacy and security concerns, concerns about inappropriate e-mail use by patients, and the creation of unrealistic expectations about physician availability. CONCLUSION: E-mail use between academic family physicians and patients was found to be much higher than shown in previous studies of Canadian physicians. This finding might have been owing to unique aspects of academic medicine, remuneration via capitation, or other factors. Efforts to increase physician use of e-mail with patients should address concerns related to privacy and security, electronic medical record integration, and financial compensation.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".