The Use of Email and Secure Messaging between Residents and Patients at St. Michael’s Family Medicine Residency Program
Bibliographic record
Abstract
Background: The use of email and secure messaging between physicians and patients is increasing in frequency. P PHowever, residents lack formal training in e-communication, patient privacy and other confidentiality issues associated with it. There is also a paucity of assessment tools and faculty feedback regarding this practice.Objective: The objective is to investigate use of email and secure messaging between patients and residents at St. Michael’s family medicine residency program and analyze educational constructs, facilitators, and barriers relevant to this practice.Methods: Three cross-sectional surveys were conducted at St. Michael’s family medicine residency program in 2018-2020. Each resident in postgraduate year 1 & 2 received an email inviting them to respond.
 Results: The prevalence of residents using email or secure messaging is increasing (47% in 2018 vs 81% in 2020). Over 86% of FM residents used hospital/clinic computers in 2020 but the proportion of residents using personal computers rose to 60% that year. A prominent barrier appears to be the ‘potential for inappropriate use by patients’, which was cited as ‘fairly’ or ‘very’ important at rates of 85.3%, 86.9%, and 73.68% in 2018, 2019 and 2020, respectively. 76.4% and 56.52% of residents cited lack of consistent advice/guidelines as a barrier in the years 2018 and 2019, respectively. The perception of support has risen (33.3% residents reporting supervisors as ‘very’ or ‘somewhat’ supportive versus 57.8% in 2020). The majority reported ‘rarely’ or ‘never’ getting feedback/guidance from their supervisors.Conclusions: Our study found an increase in the use of email and secure messaging. Residents are increasingly using their personal computers which likely reflects the increase in virtual models of care. Residents have concerns regarding the appropriate use of such messaging by patients. Lack of supervision may pose a risk of patient confidentiality/privacy breach. There is a need for curricular re-design and faculty development around this practice.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".