Opportunities to Improve Communication With Residency Applicants: Cross-Sectional Study of Obstetrics and Gynecology Residency Program Websites
Notice bibliographique
Résumé
Background: As part of the residency application process in the United States, many medical specialties now offer applicants the opportunity to send program signals that indicate high interest to a limited number of residency programs. To determine which residency programs to apply to, and which programs to send signals to, applicants need accurate information to determine which programs align with their future training goals. Most applicants use a program's website to review program characteristics and criteria, so describing the current state of residency program websites can inform programs of best practices. Objective: This study aims to characterize information available on obstetrics and gynecology residency program websites and to determine whether there are differences in information available between different types of residency programs. Methods: This was a cross-sectional observational study of all US obstetrics and gynecology residency program website content. The authorship group identified factors that would be useful for residency applicants around program demographics and learner trajectories; application criteria including standardized testing metrics, residency statistics, and benefits; and diversity, equity, and inclusion mission statements and values. Two authors examined all available websites from November 2011 through March 2022. Data analysis consisted of descriptive statistics and one-way ANOVA, with P<.05 considered significant. Results: Among 290 programs, 283 (97.6%) had websites; 238 (82.1%) listed medical schools of current residents; 158 (54.5%) described residency alumni trajectories; 107 (36.9%) included guidance related to the preferred United States Medical Licensing Examination Step 1 scores; 53 (18.3%) included guidance related to the Comprehensive Osteopathic Medical Licensing Examination Level 1 scores; 185 (63.8%) included international applicant guidance; 132 (45.5%) included a program-specific mission statement; 84 (29%) included a diversity, equity, and inclusion statement; and 167 (57.6%) included program-specific media or links to program social media on their websites. University-based programs were more likely to include a variety of information compared to community-based university-affiliated and community-based programs, including medical schools of current residents (113/123, 91.9%, university-based; 85/111, 76.6%, community-based university-affiliated; 40/56, 71.4%, community-based; P<.001); alumni trajectories (90/123, 73.2%, university-based; 51/111, 45.9%, community-based university-affiliated; 17/56, 30.4%, community-based; P<.001); the United States Medical Licensing Examination Step 1 score guidance (58/123, 47.2%, university-based; 36/111, 32.4%, community-based university-affiliated; 13/56, 23.2%, community-based; P=.004); and diversity, equity, and inclusion statements (57/123, 46.3%, university-based; 19/111, 17.1%, community-based university-affiliated; 8/56, 14.3%, community-based; P<.001). Conclusions: There are opportunities to improve the quantity and quality of data on residency websites. From this work, we propose best practices for what information should be included on residency websites that will enable applicants to make informed decisions.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».