Design and Implementation of an Online Course on Research Methods in Palliative Care: Lessons Learned
Bibliographic record
Abstract
BACKGROUND: Research capacity in palliative and end-of-life care is less than some other fields of medicine where there is a longer track record of biomedical research. Palliative medicine clinicians often receive little or no formal research training during their postgraduate education; hence, education efforts may prove pivotal to increasing palliative care research capacity. To that end, our group established a national online training program on palliative care research methodologies, called Foundations of Palliative Care Research. This report describes the development and implementation of the course, and its evaluation. To inform decisions on the overall course objectives, length, design, and implementation, formal needs assessments were conducted through surveys of Canadian palliative medicine residency program directors and of Canadian palliative medicine residents. METHODS: A 12-week, online, module-based course was designed. The first iteration of the course was offered to English-speaking palliative medicine residents from across Canada between October 2008 and March 2009. The course utilized Web-based communication methods, and was delivered using a combination of asynchronous and synchronous learning strategies and activities. RESULTS: Ten palliative care residents from different parts of the country registered and all completed the course with passing marks. Participants evaluated the course through a post course survey. The formal evaluation of the course, along with successes, challenges, and lessons applicable to future ventures, are discussed.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".