Quality and Features of Open Educational Resources (OER) in the Areas of Palliative and End-of-Life Care: An Integrative Review
Bibliographic record
Abstract
Context: Palliative and end-of-life care (PEOLC) aims to relieve pain, ease suffering, and preserve dignity for terminal ill patients. Worldwide, 14% of people in need of hospice palliative care receive it. Health and social care professionals must have up-to-date knowledge to provide quality PEOLC. Open educational resources (OERs) have the potential to increase and sustain knowledge. OERs are teaching, learning, or research materials available online with content licensed for retention (preservation), reuse, revision, remix, and redistribution (5Rs). The extent of OERs on PEOLC, their characteristics, and quality remain unknown. The purpose of this integrative review is therefore to identify OERs on PEOLC for health and social care professionals, describe their key characteristics, and assess their quality. Methods: A search strategy was conducted on multidisciplinary scientific journal articles databases using the keywords “open educational resources” and “palliative care.” A manual keyword search was also completed in OERs repositories, digital educational material repositories, and video sharing websites. Online resources had to be licensed according to the 5Rs criteria, contain exercises with feedback, be in English, French, or Spanish, be targeted to health and social care professionals, and cover PEOLC. Two independent authors selected, extracted, and assessed OER characteristics using the validated Revised Medical Education Translational Resources: Impact and Quality (rMETRIQ) scale. Results: Six OERs met the selection criteria and were analyzed. Five were in English and one in French. Two provided an introduction to PEOLC and the other four addressed specific topics such as medical aid in dying, tissue donation, and pediatric PEOLC. OERs scored from 5 to 14 on quality (maximum of 21).Often, OERs were released without a peer review or transparent user review process. Conclusion: Open Educational Resources (OERs) have become critical for knowledge transfer in the health and social sciences. We demonstrated that field-specific OERs can be identified and assessed. We discovered recurring gaps in the existing open OERs on PEOLC. Students, faculty, health and social care professionals, and resource developers will benefit from the resulting list of OERs.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".