Analysis of the Financial Resources Genetic Counseling Students Use to Fund Their Training
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".