Barriers in applying to genetic counseling Master's degree programs: Perceptions of prospective applicants when compared with Canadian admissions committee members
Bibliographic record
Abstract
The goal of this study was to identify potential barriers in applying to a genetic counseling (GC) Master's degree program to inform strategies for increasing diversity and inclusiveness in the GC student recruitment process. Participants included prospective GC program applicants and admissions committee members from the four Canadian accredited programs. The study was conducted using a quantitative survey-based approach. Twenty-five prospective applicants who previously applied to a GC Master's degree program, 26 who had not applied, and 48 admissions committee members completed the survey. The small number of positions in GC programs was perceived by all groups as highly likely to impact an applicant's ability to gain acceptance to a program as was the limited number of GC training programs. Prospective applicants perceived additional barriers as significantly more likely to impact an individual's ability to apply to/attend a program when compared with admissions committee members including: cost of the application process, the applicant being a visible minority and the applicant having a physical disability. These findings highlight a number of perceived barriers related to applying to a GC Master's degree program. To our knowledge, this is the first study surveying prospective applicants and admissions committee members on barriers faced during the application process. The data from this study can also be used to inform the application process for other health professions.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".