Selected Skill Sets as Building Blocks for High School-to-Medical School Bridge: Longitudinal Study Among Undergraduate Medical Students
Bibliographic record
Abstract
BACKGROUND: The high school-to-medical school education transition is a significant milestone in the students' academic journey, which is characterized by multiple stressors. Although this crucial transition has been repetitively explored, the concept of proactively intervening to support this transition is still novel. OBJECTIVE: In this study, we investigated the efficacy of a web-based multidimensional resilience building intervention in developing selected soft skills that are believed to drive the learner's success in any learning setting. The association between the students' academic performance over time and their proficiency in selected modules addressing skill sets, including Time Management, Memory and Study, Listening and Taking Notes, and College Transition, was also assessed to test the impact of the intervention on the students' learning. METHODS: A longitudinal study was conducted on 1 cohort of students of a Bachelor of Medicine, Bachelor of Surgery program (MBBS). The medical students were offered a learning intervention around 4 skill sets during the first year of the 6-year program. Quantitative analyses were conducted using deidentified data, relating to the students' proficiency in the 4 skill sets and to the students' academic performance: grade point average (GPA). Descriptive analyses constituted computing an overall score of skill sets' proficiency (of all 4 selected skill sets). The mean and SD (and percentage of the mean) were also calculated for each skill set component, independently, and for the overall score of skill sets' proficiency. Bivariate Pearson correlations were used to assess the extent to which the academic performance of the students can be explained by the corresponding students' level of proficiency in each skill set component and by all 4 sets together. RESULTS: Out of the 63 admitted students, 28 participated in the offered intervention. The means and SDs of the annual GPA of the students for years 1 and 2 (GPA range 1-4) were 2.83 (SD 0.74) and 2.83 (SD 0.99), respectively. The mean and SD of the cumulative GPA toward the end of year 2 was 2.92 (SD 0.70). Correlation analysis showed that the overall score of skill sets proficiency was significantly associated with the annual GPA of year 1 (r=0.44; P=.02) but was not associated with their annual GPA of year 2. The cumulative GPA (toward the end of year 2) appeared to be significantly associated with the overall score (r=0.438; P=.02). CONCLUSIONS: Developing purposefully selected skill sets among medical students holds the potential of facilitating the high school-to-medical school education transition and is likely to improve their academic performance. As the medical student progresses, the acquired skills need to be continuously reinforced and effectively built upon.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".