A Reflection Upon the Impact of Early 21st-Century Technological Innovations on Medical School Admissions
Bibliographic record
Abstract
The authors describe influences associated with the incorporation of modern technologies into medical school admissions processes. Their purpose is not to critique or support specific technologies but, rather, to prompt reflection on the evolution that is afoot. Technology is now integral to the administration of multiple admissions tools, including the Medical College Admission Test, situational judgment tests, and standardized video interviews. Consequently, today's admissions landscape is transforming into an online, globally interconnected marketplace for health professions admissions tools. Academic capitalism and distance-based technologies combine to enable global marketing and dissemination of admissions tests beyond the national jurisdictions in which they are designed. As predicted by disruptive business theory, they are becoming key drivers of transformative change. The seeds of technological disruption are present now rather than something to be wary of in the future. The authors reflect on this transformation and the need for tailoring test modifications to address issues of medical student diversity and social responsibility. They comment on the online assessment of applicants' personal competencies and the potential detriments if this method were to replace admissions methods involving human contact, thanks to the ease with which institutions can implement them without cost to themselves and without adequate consideration of measurement utility or contextual appropriateness. The authors advocate for socially responsible academic capitalism within this interconnected admissions marketplace: Attending to today's transformative challenges may inform how health professions education responds to tomorrow's admissions technologies and, in turn, how tomorrow's health professionals respond to their patients' needs.
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 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 | 0.034 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.040 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.023 | 0.037 |
| Insufficient payload (model declined to judge) | 0.006 | 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".