Mapping an undergraduate medical education curriculum against national and international palliative care reference learning objectives
Bibliographic record
Abstract
Abstract Background The teaching of palliative care competencies is an essential component of undergraduate medical education. There is significant variance in the palliative care content delivered in undergraduate medical curricula, revealing the utility of reference standards to guide curricular development and assessment. To evaluate our university’s undergraduate palliative care teaching, we undertook a curriculum mapping exercise, comparing official learning objectives to the national Educating Future Physicians in Palliative and End-of-Life Care (EFPPEC) and the international Palliative Education Assessment Tool (PEAT) reference objectives. Methods Multiple assessors independently compared our university’s UGME learning objectives with EFPPEC and PEAT reference objectives to determine the degree-of-coverage. Visual curriculum maps were created to depict in which part of the curriculum each objective is delivered and by which medical specialty. Results Of 122 EFPPEC objectives, 55 (45.1%) were covered fully, 42 (34.4%) were covered partially, and 25 (20.5%) were not covered by university objectives. Of 89 PEAT objectives, 40 (44.9%) were covered fully, 35 (39.3%) were covered partially, and 14 (15.7%) were not covered by university objectives. Conclusions The majority of EFPPEC and PEAT reference objectives are fully or partially covered in our university’s undergraduate medical curriculum. Our approach could serve as a guide for others who endeavour to review their universities’ specialty-specific medical education against reference objectives. Future curriculum development should target the elimination of identified gaps and evaluate the attainment of palliative care competencies by medical learners.
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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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".