Genetic Counseling Student Anxiety, Self-Efficacy, and Study Strategy Use for the Genetic Counseling Board Certification Exam.
Bibliographic record
Abstract
With a 76% pass rate for the 2024 American Board of Genetic Counseling (ABGC) certification exam, it's crucial to evaluate the study strategies used by candidates and their alignment with evidence-based practices. This study surveyed 84 genetic counseling students and graduates from 35 U.S. and Canadian training programs, focusing on demographic data, study strategy usage, anxiety, and self-efficacy. Study strategies were categorized into one of three groups: 1) active strategies, which promote recall and understanding, 2) passive strategies, and 3) hybrid, for strategies that involve both active and passive engagement as well as three metacognitive strategies: regulating learning, monitoring learning, and reflecting on mistakes. Results showed that the most common passive strategy was reviewing notes (33% spent over an hour/week), while flashcards were the most used active strategy (42% spent over an hour/week). Other active strategies, like self-testing (37%), elaboration (27%), and problem sets (24%), were less frequently used. These findings suggest that students invest a lot of time on flashcards that help with memorization and recall. However, increasing the use of other active strategies, which are more effective in promoting deeper understanding and application of knowledge, is particularly important given that 75-80% of questions on the ABGC board exam are application questions. Monitoring learning was the most common metacognitive strategy, with 18% dedicating over an hour/week. Correlations revealed that frequent use of metacognitive strategies was strongly associated with more time spent on both active (r = .754) and passive (r = .693) study strategies. Higher anxiety was linked to more time spent on board exam study (r = .288, p = 0.008). Additionally, anxiety and self-efficacy were moderately negatively correlated (r = -0.535, p <.001). These findings suggest that while anxiety may act as a motivator for increased study time, integrating metacognitive strategies could enhance study effectiveness and improve overall exam preparation.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".