Burden and Characteristics of Unsolicited Emails from Medical/Scientific Journals, Conferences, and Webinars to Faculty and Trainees at an Academic Pathology Department
Bibliographic record
Abstract
BACKGROUND: Professionals and trainees in the medical and scientific fields may receive high e-mail volumes for conferences and journals. In this report, we analyze the amount and characteristics of unsolicited e-mails for journals, conferences, and webinars received by faculty and trainees in a pathology department at an academic medical center. METHODS: With informed consent, we analyzed 7 consecutive days of e-mails from faculty and trainees who voluntarily participated in the study and saved unsolicited e-mails from their institutional e-mail address (including junk e-mail folder) for medical/scientific journals, conferences, and webinars. All e-mails were examined for characteristics such as reply receipts, domain name, and spam likelihood. Journal e-mails were specifically analyzed for claims in the message body (for example, peer review, indexing in databases/resources, rapid publication) and actual inclusion in recognized journal databases/resources. RESULTS: A total of 17 faculty (4 assistant, 4 associate, and 9 full professors) and 9 trainees (5 medical students, 2 pathology residents, and 2 pathology fellows) completed the study. A total of 755 e-mails met study criteria (417 e-mails from 328 unique journals, 244 for conferences, and 94 for webinars). Overall, 44.4% of e-mails were flagged as potential spam by the institutional default settings, and 13.8% requested reply receipts. The highest burden of e-mails in 7 days was by associate and full professors (maximum 158 or approximately 8200 per year), although some trainees and assistant professors had over 30 e-mails in 7 days (approximately 1560 per year). Common characteristics of journal e-mails were mention of "peer review" in the message body and low rates of inclusion in recognized journal databases/resources, with 76.4% not found in any of 9 journal databases/resources. The location for conferences in e-mails included 31 different countries, with the most common being the United States (33.2%), Italy (9.8%), China (4.9%), United Kingdom (4.9%), and Canada (4.5%). CONCLUSIONS: The present study in an academic pathology department shows a high burden of unsolicited e-mails for medical/scientific journals, conferences, and webinars, especially to associate and full professors. We also demonstrate that some pathology trainees and junior faculty are receiving an estimated 1500 unsolicited e-mails per year.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.004 | 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".