P.016 Re-norming medical education: centering patient experience and diverse bodies in Lumbar Puncture (LP) instruction
Bibliographic record
Abstract
Background: Medical curricula are often created with limited patient and student input and underrepresent certain body types. Traditional medical education often prepares learners to perform procedures, such as lumbar punctures (LPs), on a young white able-bodied 70kg male. When approaching diverse patients, this educational gap can lead to medical learners’ lack of confidence, skill, and knowledge, resulting in poor patient experiences. Methods: This co-design project involves patient and student input. We interviewed five patients who underwent LPs and explored their experience through a trauma-informed approach. To visualize landmarking across body types, we recruited nine volunteers of diverse body sizes, ages, tattoos, and skin colour (Fitzpatrick Scale). Incorporating patient narratives, as well as videos and photographs showing landmarking on diverse bodies, we crafted an online LP instructional module. Focus groups of 6-10 students will be held to collect student perception of the effectiveness of the module. Results: Our learning module and related media will be built into Western University’s Undergraduate Medical Education curriculum, available under a Creative Commons license through the Western Health Education Media Library. Conclusions: Integrating patient experience and student feedback, we are developing a comprehensive educational tool to better equip medical learners to deliver patient-centered LPs across diverse body types.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".