14 / HEALTH CARE PROFESSIONAL’S PRACTICE IN CANCER PREVENTION: SMOKING CESSATION COUNSELING AS AN EXAMPLE
Bibliographic record
Abstract
TitleCompetence based undergraduate education in palliative medicine. IntroductionIn their first year after qualification, doctors care for patients who are dying or have palliative care (PC) needs. All medical students should receive core teaching on PC. Medical students value PC education but complete education feeling underprepared to deal with PC issues. To be effective, PC training should be competence based. In Ireland, the Health Service Executive (HSE) produced a competence framework (the Framework) in 2014 to provide for core competences in PC. The Framework was designed to inform academic curricula and professional development programmes. ObjectivesTo revise and update the undergraduate palliative medicine curriculum (The Curriculum) at an Irish University, to comply with best international practice and the Framework. MethodsA systematised literature search to identify international competence based curricula in undergraduate palliative medicine. A gap analysis determined where the Curriculum met or failed to meet: - (a) the Framework,(b) best international practice. Using gap analysis, curriculum revision was performed using a consensus approach.ResultsFour relevant undergraduate medical curricula in PC were identified (UK, Canada, Australia and the European Association for Palliative Care). Each identified required knowledge, skills and attributes to satisfy core learning outcomes.ConclusionsUndergraduate medical education has historically involved student exposure to PC. It should now be competence based. A competence based PC curriculum is therefore a prerequisite. Future development is required to integrate PC principles throughout the medical curriculum and to introduce competence based assessments. Appropriate resources will be required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".