Racial diversity amongst Otolaryngology-Head and Neck Surgery programs in Canada
Notice bibliographique
Résumé
Abstract Background The Canadian landscape of racial diversity in academic OHNS programs is currently unknown, as to date Canadian medical organizing bodies have refrained from collecting race-based data. However, new policy guidelines by the Canadian Medical Association support the collection of data that may be used to support equity, diversity and inclusion programs. This study aims to describe the representation of visible minorities amongst academic OHNS departments and divisions in Canada at various levels of academic seniority. Methods An online survey was distributed to members of the 13 academic OHNS department in Canada in 2022. The survey collected demographic data as well as each participant’s self-reported race and gender. The primary outcome was the comparison of the racial demographics of Canadian academic OHNS programs to Canadian census data. Secondary outcome measures assessed how demographics varied based on academic position and gender. Simple descriptive statistics were tabulated for all demographic variables. Chi-square goodness of fit analysis was used to compare survey results to anticipated demographics based on 2016 Canadian census data. Results Of 545 surveys distributed, 224 surveys were completed (response rate of 41%); 67.9% or respondents were male and 32.1% were female. Of these respondents, 71 were residents, 26 lecturers, 54 assistant professors, 39 associate professors, and 34 full professors. There was significantly greater minority representation amongst residents (47.9%), assistant professors (39.6%), and lecturers (40.7%) compared to the Canadian population (25.3%) p < 0.001. Results also showed that there were significantly fewer female lecturers (25.9%, p = 0.01), assistant professors (31.5%, p = 0.006), and full professors (2.9%, p < 0.001) compared to an assumed even proportion of men and women in the population. Conclusions Academic OHNS programs in Canada are more racially diverse than the Canadian population. However, women continue to be under-represented in more senior positions, especially women who are visible minorities. Further investigation into the systemic factors that may contribute to this disparity is needed as well as effective ways to promote diversity amongst academic OHNS departments at all levels of academic seniority. Graphical abstract
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».