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Record W7064034358

Analysis of the Financial Resources Genetic Counseling Students Use to Fund Their Training

2025· other· en· W7064034358 on OpenAlexaboutno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2025
Typeother
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsDisadvantagedDebtQuarter (Canadian coin)Training (meteorology)Service (business)Default
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.021
GPT teacher head0.250
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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