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Enregistrement W2239106233 · doi:10.1200/jop.2015.006429

ReCAP: Social Media Use Among Physicians and Trainees: Results of a National Medical Oncology Physician Survey

2016· article· en· W2239106233 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.
aboutLe titre ou le résumé porte un signal canadien du lexique géographique.

Notice bibliographique

RevueJournal of Oncology Practice · 2016
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSocial Media in Health Education
Établissements canadiensPrincess Margaret Cancer CentreQueen's UniversityUniversity of WaterlooUniversity of CalgarySt. Michael's Hospital
Organismes subventionnairesnon disponible
Mots-clésSocial mediaMentorshipMedicineOncologyInternal medicineDescriptive statisticsHealth careFamily medicineMedical educationWorld Wide WebStatistics

Résumé

récupéré en direct d'OpenAlex

QUESTION ASKED: To what extent, and for what purpose, do oncology physicians and physicians-in-training use Web-based social media? SUMMARY ANSWER: Despite the ability of social media to enhance collaboration and knowledge dissemination among health care providers, this cohort survey study identified an overall low use of social media among oncologists, and significant generational gaps and differences in patterns of use. METHODS: A nine-item survey was designed using a survey-generating Web site (SurveyMonkey) and was distributed securely via weekly e-mail messages to 680 oncology physicians and physicians-in-training from July 2013 through September 2013. All responses were received anonymously. Results were analyzed and are reported using descriptive statistics. RESULTS: Of 680 surveys sent, 207 were completed, for a response rate of 30.4%. Social media were used by 72% of our survey respondents (95% CI, 66% to 78%; Table 1 ). Results were cross tabulated by age, which revealed a significant difference in social media use by age group, with 89% of trainees, 93% of fellows, and 72% of early-career oncologists reporting social media use, compared with only 39% of mid-career oncologists (P < .05). Respondents reported using each social media platform for either personal or professional purposes, but rarely both. When respondents were questioned regarding barriers to social media use and their hesitations around joining a medically related social media site, the majority (59%) answered, “I don't have enough time.” [Table: see text] BIAS, CONFOUNDING FACTOR(S), DRAWBACKS: This study was conducted online, via e-mail. Therefore, respondents may represent a subpopulation of individuals who already prefer using Web-based technologies and may be more inclined to use social media, compared with individuals who do not use e-mail and were, by default, excluded from the study. We assumed, in designing this study, that the proportion of practicing oncology physicians who do not use e-mail is low. Although our sample size is small, it does represent one third of all registered medical oncologists in Canada. Finally, the high percentage of medical oncologist respondents and the concomitantly low fraction of respondents from other specialties may mean these results are more telling of social media habits in the aforementioned demographic rather than other oncology specialties. REAL-LIFE IMPLICATIONS: Our study revealed that oncology physicians and physicians-in-training who participate in Web-based social networking are largely within the younger age cohorts, whereas mid-career oncologists (age 45 to 54 years) are largely absent from the social media scene. Gaps in social networking use between younger physicians and trainees and older generations of physicians may result in critical gaps in communication, collaboration, and mentorship between these demographics. It is hoped that with further research into understanding patterns of use and limitations, medical professionals and trainees may increase their use of social media for networking, education, mentorship, and improved patient care.

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.

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)0,015
score de la tête « metaresearch » (Gemma)0,281
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,718
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0150,281
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,002
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
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 Opus0,210
Tête enseignante GPT0,490
Écart entre enseignants0,281 · 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