Did non-standard undergraduate medical degrees widen access at eighteen British medical schools (2002 and 2011)? :The impact of the extended-entry and graduate-entry degrees on the demography of medical school applications and accepted applicants, in relation to standard-entry degrees at eighteen British medical schools
Bibliographic record
Abstract
In 1997, the MWASC reported a lack of diversity amongst undergraduate medical students. Three years later, four-year Graduate-entry (GEM) degrees were introduced, followed by six-year Extended-entry degrees in 2001. These had a widening access remit, with EXT degrees using contextual admissions to attract students from disadvantaged backgrounds and GEM degrees providing a 'second chance' for mature students with a degree to study medicine. This quantitative research utilised the novel approach of FOI requests to obtain secondary data relating to applications and admissions at the eighteen British medical schools which ran EXT and/or GEM degrees alongside pre-existing Standard-entry (STN) medical degrees between 2002 and 2011. Using three independent variables (NS-SEC, POLAR and last institution attended) applications and admissions to the EXT and GEM degrees have been explored in relation to STN degrees, to ascertain if they have fulfilled their widening access remit.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".