MétaCan
Menu
Retour à la cohorte
Enregistrement W4413418906 · doi:10.1213/xaa.0000000000002047

Solicitation by Spam: A Cross-Sectional Study of Predatory Publisher E-mails Received By Two Anesthesiologist Clinician-Scientists

2025· article· en· W4413418906 sur OpenAlexaffabout
Nikesh Chander, Olivier Brandts‐Longtin, Daniel I. McIsaac, Manoj M. Lalu

Notice bibliographique

RevueA&A Practice · 2025
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSocial Media in Health Education
Établissements canadiensUniversity of OttawaOttawa Hospital
Organismes subventionnairesnon disponible
Mots-clésInternet privacyMedicinePsychologyAdvertisingBusinessComputer science

Résumé

récupéré en direct d'OpenAlex

Predatory journals (ie, entities that prioritize self-interest at the expense of scholarship) are believed to primarily acquire submissions via unsolicited e-mails sent to researchers in bulk.1 These solicitation e-mails place a substantial burden on researchers and clinicians,2,3 as an estimated US$1.1 billion is lost globally in wasted time every year.4 Moreover, predatory e-mails can deceive even senior researchers to submit their work to dubious entities, potentially harming their reputation and contributing to unethical dissemination of research involving people and animals.5,6 The content and burden of predatory e-mails has not been studied within the field of anesthesiology, although they have been characterized in other fields (eg, oncology, orthodontics, surgery).7–9 Given the thousands of articles that we have identified in predatory anesthesia journals,10 we believe that further characterization of predatory e-mails in the field of anesthesiology will help reduce submissions to these predatory journals. The objective of this study was therefore to characterize predatory e-mail solicitations received by 2 anesthesiologist clinician-scientists. METHODS The study protocol was published on the Open Science Framework (doi: 10.17605/OSF.IO/258WG). We collected all unsolicited e-mails from academic journals received by 2 anesthesiologist clinician-scientists at The Ottawa Hospital Research Institute (M.M.L. and D.I.M.) on their respective institutional accounts over a 28-day period (July 2021). M.M.L. and D.I.M. are both mid-career clinician-scientists with established publication records in anesthesia and biomedical journals, and have diverse research interests including translational research, publication science, perioperative care of older adults, and prehabilitation. E-mails were screened by a single reviewer (N.C.). We included all e-mails that solicited a journal submission, from both presumed predatory and non-predatory journals. We excluded all other e-mails. Data from the included e-mails were extracted by a single reviewer (N.C.) with a second reviewer (M.M.L.) auditing a random sample of 20%. Abstracted data included 3 domains: (1) e-mail characteristics, (2) solicitation characteristics, and (3) journal/publisher characteristics (see Supplemental Digital Content 1, Supplemental Table 1, https://links.lww.com/AACR/A565 for the full extraction sheet). Presumed predatory e-mails were defined as those from sources (ie, journals or publishers) not indexed in the Directory of Open Access Journals (DOAJ) or from sources not listed as a member of the Committee on Publication Ethics (COPE). Indexing in the DOAJ requires that a journal be peer-reviewed and open access, whereas membership in COPE requires the journal to demonstrate adherence to high standards in publication ethics. Following e-mail data extraction, the 2 anesthesiologist clinician-scientists (M.M.L. and D.I.M.) were each randomly assigned 30 e-mails addressed to them and asked to determine the relevance of the journal’s title to their respective research interests using a 5-point Likert scale. Counts and descriptive statistics were used to identify common characteristics of solicitations. Direct comparisons were made between presumed predatory and non-predatory e-mails using Firth’s logistic regression for 4 a priori selected objective e-mail characteristics: (1) incorrect naming of the recipient, (2) presence of the word “greetings,” (3) obvious grammar mistakes, and (4) requesting submission via e-mail. Likert scales were analyzed by median and interquartile range. RESULTS Five hundred forty-six unsolicited e-mails were received by the 2 anesthesiologist clinician-scientists over the 28-day period. These e-mails were sent from 84 unique publishers (Supplemental Digital Content 1, Supplemental Table 2, https://links.lww.com/AACR/A566). We analyzed 452 messages, including 430 presumed predatory e-mails (Supplemental Digital Content 1, Supplemental Figure 1, https://links.lww.com/AACR/A567). Presumed predatory e-mails had several common characteristics (Table). Of the 311 (72%) presumed predatory e-mails that named the recipient, 147 (47%) named the recipient incorrectly. The word “greetings” appeared in 167 (39%) of presumed predatory e-mails. The grammar of the predatory e-mails was generally poor as 405 (94%) contained at least one obvious grammatical error. The presumed predatory e-mails also frequently requested submission directly via e-mail (202, 47%). All 4 of these objective characteristics, when present, were significantly associated with the solicitation being from a presumed predatory journal (Supplemental Digital Content 1, Supplemental Figure 2, https://links.lww.com/AACR/A568). Another common characteristic was the use of explicit flattery, which was present in 213 (50%) of predatory e-mails (eg, descriptors such as “most esteemed”). Table. - Characteristics of Presumed Predatory and Non-Predatory E-mails. Predatory N (%) Non-Predatory N (%) E-mail Characteristics E-mail in English 429 (99.8) 22 (100.0) Who was the e-mail sent to? Dr Lalu 167 (38.8) 6 (27.3) Dr McIsaac 263 (61.2) 16 (72.7) E-mail flagged as potentially wanted/reputable 2 (0.5) 11 (50.0) Recipient named in e-mail 311 (72.3) 14 (63.6) Recipient named incorrectly 147 (47.3) 1 (7.1) Personalized subject line 74 (17.2) 2 (9.1) Unprofessional e-mail address 8 (1.9) 0 (0.0) Sent on behalf of a legitimate researcher 13 (3.0) 7 (31.8) “Greetings” used in the e-mail 167 (38.8) 0 (0.0) Content of the e-mail personalized 79 (18.4) 1 (4.5) Obvious flattery 213 (49.5) 3 (13.6) Grammatical mistakes 405 (94.2) 4 (18.2) Multiple fonts used in an unprofessional manner 136 (31.6) 1 (4.5) Country of origin listed 232 (54.0) 16 (72.7) United States 200 (86.2) 1 (6.3) Japan 18 (7.8) 0 (0.0) India 8 (3.4) 0 (0.0) Italy 3 (1.3) 0 (0.0) England 1 (0.4) 9 (56.3) Georgia 1 (0.4) 0 (0.0) Latvia 1 (0.4) 0 (0.0) China 0 (0.0) 1 (6.3) Germany 0 (0.0) 2 (12.5) Switzerland 0 (0.0) 3 (18.8) Street address listed 162 (37.7) 15 (68.2) E-mail includes option to unsubscribe 241 (56.0) 20 (90.1) E-mail uses graphics 12 (2.8) 18 (81.8) Solicitation Characteristics Nature of the E-mail Request for journal submission 430 (100.0) 22 (100.0) Advertising of journal 1 (0.2) 4 (18.2) Invitation to join editorial board 16 (3.7) 0 (0.0) Invitation to be guest editor 1 (0.2) 0 (0.0) Invitation to review 0 (0.0) 2 (9.1) Is there a submission deadline 223 (51.9) 12 (50.0) Median (range) days after e-mail receipt 15 (0–103) 165 (76–178) Solicitation advertises quick turn around 47 (10.9) 10 (45.5) Method of submission E-mail 128 (29.8) 0 (0.0) Online portal 60 (14.0) 13 (59.1) Either 74 (17.2) 0 (0.0) Not specified 168 (39.1) 6 (27.3) Not relevant 0 (0.0) 3 (13.6) Solicitation mentioned publication fee 55 (12.8) 11 (50.0) No fee 16 (29.9) 1 (9.1) Discounted fee 27 (49.1) 8 (72.7) Specific amount listed 10 (18.2) 2 (18.2) Median (range) [USD] 399 (149–608.78) 2385 (2332–2438) Fee is negotiable 2 (3.6) 0 (0.0) Journal and Publisher Characteristics Solicitation claims peer review 90 (20.9) 15 (68.2) Solicitation claims open access 84 (19.5) 13 (59.1) Solicitation claims indexing 60 (14.0) 9 (40.9) ISSN reported 214 (49.8) 2 (9.1) Website given 223 (51.9) 22 (100.0) Impact factor mentioned 137 (31.9) 12 (54.5) Journal/publisher in DOAJ 1 (0.2) 22 (100.0) Presumed predatory solicitations regularly used time limitations to pressure a quick response. These e-mails often gave a deadline to either submit an article, receive a discount, or reply to the e-mail (223, 52%). The median time between e-mail receipt and the deadline was 15 days (range 0–103). Similarly, 47 (11%) presumed predatory e-mails used language that advertise rapid turnaround of submissions. The sources of the presumed predatory e-mails usually lacked transparency, or made false/misleading claims, regarding their publishing practices. Ninety presumed predatory e-mails claimed peer review (21%), 84 (20%) claimed to be open access, and 60 (14%) claimed indexing. Some sources claimed indexing in “databases” that are not considered indexing services (eg, Crossref, International Committee of Medical Journal Editors). Of the 137 (32%) presumed predatory e-mails that presented an impact factor, only one presented a Clarivate-calculated impact factor. Across the 60 randomly selected e-mails assessed for relevance by the 2 anesthesiologist clinician-scientists, the median (IQR) Likert score was 2 (1–3), indicative of a journal title that is “irrelevant” to the respective researcher’s discipline. DISCUSSION We identified a substantial burden of predatory e-mails received by 2 anesthesiologist clinician-scientists. Our analysis provides a novel characterization of predatory e-mails in anesthesia and identifies 4 objective characteristics commonly found in these messages. Although these features have been noted in other fields (oncology,7 orthodontics,8 surgery9), our analysis confirms their significant association with presumed predatory journals. These objective characteristics can help researchers to effectively differentiate predatory e-mails from desired solicitations (Figure). Further, these characteristics can be used to inform the development of tools (eg, improved firewalls to block predatory e-mails) and educational resources to strengthen our collective response to predatory journals.11 To increase the generalizability of our findings, future studies should assess solicitation e-mails received by anesthesiologists, researchers, and trainees with differing backgrounds.Figure.: Is this solicitation from a potentially predatory source?This manuscript was handled by: Charles Emala, MS, MD.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,053
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,291
Score d'incertitude au seuil0,964

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0060,053
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,000
Communication savante0,0000,002
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,107
Tête enseignante GPT0,518
Écart entre enseignants0,411 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2025
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueA&A PracticeMême sujetSocial Media in Health EducationTravaux en français237 207