“A Chance To Show Yourself” – how do applicants approach medical school admission essays?
Bibliographic record
Abstract
BACKGROUND: Although essay questions are used in the admissions process in many medical schools, there has been little research on how applicants respond to essay questions. AIMS: The purpose of this study was to explore how applicants to medical school approach essay questions used in the selection process. METHODS: Qualitative analysis was conducted on 240 randomly selected essays written by individuals applying to a single Canadian medical school in 2007 using a modified grounded theory approach to develop a conceptual framework which was checked in interviews with applicants. RESULTS: Three core variables were identified: "balancing service and reward," "anticipating the physician role," and "readiness." We described the overall approach of applicants as "taking stock," writing about their journeys to the selection process, their experiences of the process itself, and about their anticipated future in medicine. CONCLUSION: Our findings suggest a disconnect between the approach of the applicants (to "show themselves" and be selected as individuals) and the stated intent of the process (to select applicants based on "objective" criteria). Our findings raise important questions about how applicants represent themselves when applying for medical school and suggest that it is important to understand the applicant's point of view when developing questions for selection processes.
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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 | 0.015 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".