E-Mail Communication Practices and Preferences Among Patients and Providers in a Large Comprehensive Cancer Center
Bibliographic record
Abstract
PURPOSE: Little is known about how electronic mail (e-mail) is currently used in oncology practice to facilitate patient care. The objective of our study was to understand the current e-mail practices and preferences of patients and physicians in a large comprehensive cancer center. METHODS: Separate cross-sectional surveys were administered to patients and physicians (staff physicians and clinical fellows) at the Princess Margaret Cancer Centre. Logistic regression was used to identify factors associated with current e-mail use. Record review was performed to assess the impact of e-mail communication on care. RESULTS: The survey was completed by 833 patients. E-mail contact with a member of the health care team was reported by 41% of respondents. The team members contacted included administrative assistants (52%), nurses (45%), specialist physicians (36%), and family physicians (18%). Patient factors associated with a higher likelihood of e-mail contact with the health care team included younger age, higher education, higher income, enrollment in a clinical trial, and receipt of multiple treatments. Eighty percent of physicians (n = 63 of 79) reported previous contact with a patient via e-mail. Physician factors associated with a greater likelihood of e-mail contact with patients included older age, more senior clinical position, and higher patient volume. Nine hundred sixty-two patient records were reviewed, with e-mail correspondence documented in only 9% of cases. CONCLUSION: E-mail is commonly used for patient care but is poorly documented. The use of e-mail in this setting can be developed with appropriate guidance; however, there may be concerns about widening the gap between certain groups of patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".