Palliative Care Always as a massive open online course (MOOC) to build primary palliative care in a global audience.
Bibliographic record
Abstract
123 Background: Primary palliative care (PC) is critical to improve access to PC from the point of diagnosis. Still, barriers exist to providing primary PC worldwide, including a lack of awareness, time, and training. Interactive online learning experiences can help overcome these. This project describes a massive open online course (MOOC)--Palliative Care Always--designed to build primary PC skills in a global audience. Methods: A team of PC providers and online instructional experts developed 12 modules that included: patient scenes, brief lectures, empathy exercises, and Google Hangout discussions. Course objectives included awareness of PC, practicing effective communication skills, basic symptom assessment and management. The target audience included oncology clinicians; secondary audience included patients and families. The MOOC launched January-April 2016. Participant engagement, satisfaction and self-reported knowledge were assessed through pre- and post-surveys. Multiple choice assessments captured knowledge gain. Follow-up assessments will be distributed three months post-MOOC. Results: By April 2016, the course reached 1,300 participants from 91 countries. 54% were from the US, followed by India, Brazil, and Canada. 76% were healthcare professionals, the majority being nurses (40%), physicians (19%) and social workers (13%). The remaining 24% included patient, caregivers, and others interested in PC. Top reasons for enrolling were interest in PC, personal growth and job relevance. On average, 27% of enrollees actively engaged week-over-week. Eighty-six percent of respondents were “very satisfied” with the amount learned, and over 50% cited learning “a great deal” in: communicating difficult news, goals of care, psychosocial and hospice care. 93% cited being “very likely” to recommend the course. Conclusions: Interactive MOOC experiences have the potential to build PC awareness, primary skills and global PC networks. Upcoming iterations will incorporate: accommodations for varying levels of PC knowledge; additional opportunities for interaction between participants, including social networks; blended learning; and evaluation of impact on practice and healthcare outcomes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.009 | 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".