Feasibility of a massive online open course to teach skills in primary palliative care for a global audience.
Bibliographic record
Abstract
111 Background: Palliative Care Always (PCA) is a massive open online course (MOOC), through Stanford Online, that teaches primary palliative care. Our goal was to provide an accessible avenue for palliative care education on a global level. This is to address persistent and considerable gaps in primary palliative care due to lack of specialist trained experts and training opportunities. Methods: A team of providers including palliative care physician, advanced care nurse practitioner, priest and social worker developed 12 modules covering the scope of palliative care across their specialties. Topics ranged from communication of bad news to hospice initiation, while following the case of an oncology patient. Components include (1) Reflection on assigned readings (2) Patient scenes (3) Multiple choice assessments and (4) Group discussions. Participants were surveyed prior to and on completion of the 12-week course. Results: PCA enrolled 721 applicants from 56 countries. Participants from the US represented 51% of the total cohort. Next highest enrollment was from Brazil, India and Canada. Median age was 40 years. 51% of surveyed participants held advanced degrees and 38% held bachelor’s degrees. At the time of enrollment, 70% of participants were employed, 76% of whom worked in healthcare or social assistance industries. 44% of participants identified themselves as having some level of teaching experience. 75% of teachers reported never covering material on palliative care. The most popular reasons for taking the course included general interest, personal growth and relevance to employment. Post assessment surveys showed 95% of enrollees had a positive experience and were satisfied with what they learned; the most useful module was communication of bad news. Conclusions: Based on participant reports, effective palliative care training and exposure on a global level is feasible and acceptable with a MOOC such as PCA. Participants were from diverse backgrounds and found the course relevant and helpful. Future directions include specialty-specific courses, with a Hepatology course currently underway, evaluation of the impact on practice outcomes, and customized cases for individualized training.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".