Advantages and Disadvantages of Educational Email Alerts for Family Physicians: Viewpoint
Bibliographic record
Abstract
BACKGROUND: Electronic knowledge resources constitute an important channel for accredited Continuing Medical Education (CME) activities. However, email usage for educational purposes is controversial. On the one hand, family physicians become aware of new information, confirm what they already know, and obtain reassurance by reading educational email alerts. Email alerts can also encourage physicians to search Web-based resources. On the other hand, technical difficulties and privacy issues are common obstacles. OBJECTIVE: The purpose of this discussion paper, informed by a literature review and a small qualitative study, was to understand family physicians' knowledge, attitudes, and behavior in regard to email in general and educational emails in particular, and to explore the advantages and disadvantages of educational email alerts. In addition, we documented participants' suggestions to improve email alert services for CME. METHODS: We conducted a qualitative descriptive study using the "Knowledge, Attitude, Behavior" model. We conducted semi-structured face-to-face interviews with 15 family physicians. We analyzed the collected data using inductive-deductive thematic qualitative data analysis. RESULTS: All 15 participants scanned and prioritized their email, and 13 of them checked their email daily. Participants mentioned (1) advantages of educational email alerts such as saving time, convenience and valid information, and (2) disadvantages such as an overwhelming number of emails and irrelevance. They offered suggestions to improve educational email. CONCLUSIONS: The advantages of email alerts seem to compensate for their disadvantages. Suggestions proposed by family physicians can help to improve educational email alerts.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".