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Enregistrement W4377142772 · doi:10.2196/39831

Influence of Social Media on Applicant Perceptions of Anesthesiology Residency Programs During the COVID-19 Pandemic: Quantitative Survey

2023· article· en· W4377142772 sur OpenAlexvenueno aff
Tyler Dunn, Shyam Patel, Adam J. Milam, Joseph C. Brinkman, Andrew Gorlin, Monica W. Harbell

Notice bibliographique

RevueJMIR Medical Education · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSocial Media in Health Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSocial mediaAnesthesiologyEthnic groupMedicinePandemicDescriptive statisticsMedical educationFamily medicinePsychologyCoronavirus disease 2019 (COVID-19)SociologyPolitical science

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Social media may be an effective tool in residency recruitment, given its ability to engage a broad audience; however, there are limited data regarding the influence of social media on applicants' evaluation of anesthesiology residency programs. OBJECTIVE: This study evaluates the influence of social media on applicants' perceptions of anesthesiology residency programs during the COVID-19 pandemic to allow programs to evaluate the importance of a social media presence for residency recruitment. The study also sought to understand if there were differences in the use of social media by applicant demographic characteristics (eg, race, ethnicity, gender, and age). We hypothesized that given the COVID-19 pandemic restrictions on visiting rotations and the interview process, the social media presence of anesthesiology residency programs would have a positive impact on the recruitment process and be an effective form of communication about program characteristics. METHODS: All anesthesiology residency applicants who applied to Mayo Clinic Arizona were emailed a survey in October 2020 along with statements regarding the anonymity and optional nature of the survey. The 20-item Qualtrics survey included questions regarding subinternship rotation completion, social media resource use and impact (eg, "residency-based social media accounts positively impacted my opinion of the program"), and applicant demographic characteristics. Descriptive statistics were examined, and perceptions of social media were stratified by gender, race, and ethnicity; a factor analysis was performed, and the resulting scale was regressed on race, ethnicity, age, and gender. RESULTS: The survey was emailed to 1091 individuals who applied to the Mayo Clinic Arizona anesthesiology residency program; there were 640 unique responses recorded (response rate=58.6%). Nearly 65% of applicants reported an inability to complete 2 or more planned subinternships due to COVID-19 restrictions (n=361, 55.9%), with 25% of applicants reporting inability to do any visiting student rotations (n=167). Official program websites (91.5%), Doximity (47.6%), Instagram (38.5%), and Twitter (19.4%) were reported as the most used resources by applicants. The majority of applicants (n=385, 67.3%) agreed that social media was an effective means to inform applicants, and 57.5% (n=328) of them indicated that social media positively impacted their perception of the program. An 8-item scale with good reliability was created, representing the importance of social media (Cronbach α=.838). There was a positive and statistically significant relationship such that male applicants (standardized β=.151; P=.002) and older applicants (β=.159; P<.001) had less trust and reliance in social media for information regarding anesthesiology residency programs. The applicants' race and ethnicity were not associated with the social media scale (β=-.089; P=.08). CONCLUSIONS: Social media was an effective means to inform applicants, and generally positively impacted applicants' perception of programs. Thus, residency programs should consider investing time and resources toward building a social media presence to improve resident recruitment.

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)0,004
score de la tête « metaresearch » (Gemma)0,055
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 enseignants0,284
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,055
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0010,002
Communication savante0,0000,000
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 Opus0,168
Tête enseignante GPT0,503
Écart entre enseignants0,335 · 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

Citations11
Publié2023
Routes d'admission1
Résumé présentoui

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