Being there in a crisis: increasing access to the paramedic profession for BME communities
Bibliographic record
Abstract
In 2013-14, the Higher Education Statistics Agency (HESA) reported that only 3.4% of paramedic learners with HEIs were from a minority ethnic background. Similarly, the National Ambulance Diversity Forum found that only 7.4% of paramedics employed nationally are from a minority ethnic background). Promoting ethnic diversity in paramedic education and the profession is beneficial as evidence suggests that having a workforce that represents the communities those health professionals serve reduces health inequalities. This project aims to improve access, participation and progression of minority ethnic groups, thus across the career cycle from pre-university to post-registration. Based on an asset-based community development model, the project offers enhancement opportunities for students to focus on their existing strengths and motivation, develop a sense of agency, build a supportive learning community, and empower each other to achieve their academic and professional potential. The interventions will therefore be co-designed with stakeholders (e.g., students, paramedics and community) as part of the project cycle.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".