(L)earning: Exploring the value of paid roles for medical students
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it