Distance learning for updating health professionals in palliative care: a systematic review
Bibliographic record
Abstract
OBJECTIVES: To review literature regarding online educational initiatives in palliative care which are targeted to update health professionals and prepare distance courses suitable for a Brazilian context. METHODS: 7 databases (PubMed, Cochrane Library, LILACS, SCIELO, CINAHL, Science Direct and Scopus) were reviewed for published papers between January 2004 and August 2014 using the PRISMA methodology. Included studies focused on health professionals and had at least part of the course in a distance learning approach. RESULTS: The UK, the USA, Canada and Australia stood out within the palliative care research papers. Among the 590 articles chosen, only 14 papers were included in this review due to the inclusion criteria. 9 used a mixed approach and 5 used online methods. The length of the courses, however, varied extensively and several methods were found to have been employed for teaching purposes, including videos, audio, images, poetry and simulation cases. CONCLUSIONS: Although the literature is abundant in this area, there is limited research exploring the construction process of courses and how they can be applied to countries with limited resources. It is important to highlight, however, that the mixed teaching strategy, which allows for theoretical and practical activities at a low cost, is imperative for countries with limited resources in healthcare. Thus, this review can support new initiatives around the world, particularly in the low-income and middle-income countries.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".