(L)earning: Exploring the value of paid roles for medical students
Bibliographic record
Abstract
BACKGROUND: The Medical Student Technician (MST) role is a paid position established in Northern Ireland in 2020. The Experience-Based Learning (ExBL) model is a contemporary medical education pedagogy advocating supported participation to develop capabilities important for doctors-to-be. In this study, we used the ExBL model to explore the experiences of MSTs and how the role contributed to students' professional development and preparedness for practice. METHODS: A convenience sampling strategy was used to recruit a total of 17 MSTs in three focus groups. Semi-structured interviews were transcribed verbatim and analysed using the ExBL model as a framework. Transcripts were independently analysed and coded by two investigators and discrepancies resolved with the remaining investigators. RESULTS: The MST experiences reflected the various components of the ExBL model. Students valued earning a salary; however, what students earned transcended the financial reward alone. This professional role enabled students to meaningfully contribute to patient care and have authentic interactions with patients and staff. This fostered a sense of feeling valued and increased self-efficacy amongst MSTs, helping them acquire various practical, intellectual and affective capabilities and subsequently demonstrate an increased confidence in their identities as future doctors. CONCLUSION: Paid clinical roles for medical students could present useful adjuncts to traditional clinical placements, benefiting both students and potentially healthcare systems. The practice-based learning experiences described appear to be underpinned by a novel social context where students can add value, be and feel valued and gain valuable capabilities that better prepare them for starting work as a doctor.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".