AAPM WGWMRSC Report 420: chapter climate check: Mixed methods analysis of survey responses
Notice bibliographique
Résumé
INTRODUCTION: The American Association of Physicists in Medicine (AAPM) recently shared results and recommendations from its first Equity, Diversity, and Inclusion (EDI) Climate Survey, which was designed to assess the climate at the workplace, the AAPM organization, and the AAPM regional chapter level. This work further explores the status of EDI at the regional chapter level. METHODS: AAPM's EDI Survey was distributed to 5500 members and had a response rate of 25%. In the survey, three open-ended comment boxes were provided for feedback, including one for regional AAPM members. Sixty-four percent of respondents indicated they were part of a regional chapter, and 6% provided written responses to the regional chapter question. Responses were analyzed using a mixed methods approach with an exploratory sequential design. Two phases were conducted; the first relied on a Grounded Theory quantitative systemic approach, and the second applied qualitative analysis. Chapter member demographic data were collected to support findings. RESULTS: Survey respondents provided open comments and feedback on their regional chapter's climate. Data are summarized as five themes: positive experiences, negative experiences, challenges within chapters, diversity and inclusion, and changes observed. Experiences of regional chapters were rated positively by 75% of respondents. Respondents found their chapters were welcoming, and some noted their great chapter leadership. A number of incidents of sexual harassment, bullying, and discrimination incidences were also shared. Other respondents observed exclusion based on their gender, race, highest degree, and medical physics specialty. Chapter leadership data aligned with these claims, with most leaders to-date being white males, doctoral degree holders, and/or specializing in radiation therapy. CONCLUSION: AAPM chapters provide rewarding professional opportunities. This study has highlighted positive and negative experiences reported by its members. The major themes identified can guide chapter leaders to continue to cultivate welcoming communities for regional AAPM members.
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 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 | 0,015 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».