Analysis of the Financial Resources Genetic Counseling Students Use to Fund Their Training
Notice bibliographique
Résumé
Previous studies investigating the financial resources genetic counseling (GC) trainees use to fund their training are limited in number, with the most recent study completed in 2014. This study aimed to evaluate the various financial resources that GC trainees use to fund their training and make comparisons among different groups via an anonymous online survey to graduates of GC training programs between 2019 and 2026. Of the 477 responses eligible for analysis, 63.6% were from 2019 – 2024 graduates and the rest were from current GC trainees. A quarter of participants self-identified as Disadvantaged (per NIH definition) and 11.5% identified as having a disability. Additionally, 15.9% of participants were classified as historically underrepresented in medicine (hURM). One notable theme seen was the need of financial education for some trainees. A surprisingly high proportion of participants responded “No” or “I don’t know” when asked if they were aware of (44%) or had access to (50.7%) financial advising/education or were aware if their GC training program (GCTP) offered this service (71.3%). Additionally, 23% of participants reported not knowing about federal or other loan forgiveness programs despite reporting debt from training. Among the different subgroup comparisons, many notable differences were found. Overall, Disadvantaged participants were more likely to identify as non-White (p<0.001) and hURM (p<0.001) than non-Disadvantaged participants. Additionally, these participants were less likely to have access to familial financial assistance (p<0.001 for non-Whites, p=0.036 for Whites) than their counterparts. Regardless of Disadvantaged status, non-White participants were more likely to report over $150,000 in educational debt (p=0.006 for Disadvantaged; p=0.019 for non-Disadvantaged) and more likely to report using credit cards to pay for tuition (p=0.017 for Disadvantaged; p=0.03 for non-Disadvantaged). Furthermore, Disadvantaged non-White participants were less likely to use familial financial assistance (p=0.022) and more likely to use loans (p<0.001) to pay for tuition than non-Disadvantaged Non-White participants, who were more likely to use government assistance in general (p<0.001). In contrast, non-Disadvantaged White participants were more likely to report over $150,000 in debt than their Disadvantaged White counterparts. When analyzing participants by hURM status, Disadvantaged hURM participants were more likely to use credit cards to pay for tuition (p=0.036) than Disadvantaged non-hURM participants. Among the non-Disadvantaged, hURM participants were more likely to report feeling their post-graduate salary was insufficient (p=0.022) and report using government assistance in general (p<0.001). In addition, they specifically reported using government assistance to pay for housing (p<0.001) and food (p<0.001) more than their counterparts. Lastly, when analyzing participants by Disability Status, Disabled individuals were less likely to report being employed than non-Disabled individuals in their first (p<0.001) and second years (p<0.001) of training and were more likely to report feeling their post-graduate salary was insufficient (p=0.008). This study is revealing current strategies for covering the costs of GCTP which hopefully will contribute to increasing diversity in this field, illustrate the need for more funding for GC training, and improve the experiences of prospective, incoming, and current GC trainees.
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 | 0,003 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
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 ».