Online learning in palliative care education of undergraduate medical students: a realist synthesis
Bibliographic record
Abstract
Background: Although training in palliative care (PC) is increasingly frequent in medical schools, some barriers still hamper the design and implementation of effective educational programs. Information Technology-based distance learning (IT-DL) might contribute to the development of appropriate knowledge on PC in students, but it is still not clear how to best develop such curricula and how to deliver methodologically sound learning activities, allowing students to work on the complex skills required in PC. Objectives: To describe how internet-based education can be used in undergraduate medical PC education, in terms of realist theorization. Design: A realist review was carried out, producing a framework – or, in the terms of a realist review, a theory – focusing on finding out what might work, for whom, and in which circumstances, describing these variables in terms of Contexts, Mechanism, and Outcomes. Methods: An international group of experts of PC education assessed the relevance and pertinence of 256 articles resulting from systematic retrieval of literature and expert suggestions. Results: The final synthesis, mainly informed by the 43 articles rated as most relevant, is presented in propositions regarding three groups: (1) Educational theory, where (a) Cognitivism (learning as an increase in knowledge); (b) Constructivism (learning as a social, cultural, and negotiated process); and (c) Behaviorism (learning as an observable modification of behaviors) appear to be consistent with the learning outcome of PC. (2) Desired effect of the technology, suggesting the simple use of IT-DL is not an additional value per se, as it should overcome objective limits set for face-to-face activities. (3) Contextualization and duration of the curricular activity, suggesting PC training activities should be included in an organic and legitimate way in the overall curriculum. Conclusion: This field is expected to experience huge growth soon, and present and future research could use a realist approach like the one here presented to make sense of all the different variables involved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".