The development of a European elearning cultural competence education project and the creation of it’s underpinning literature based theoretical and organising framework
Bibliographic record
Abstract
The EU have set standards in relation to cultural competence, and findings from previously funded EU commission projects have illuminated an extensively developed body of knowledge in this area in relation to healthcare. Evidence from contemporary literature shows that education interventions have a positive impact on the cultural competence of health care professionals. Nonetheless, short accessible resources that can be used flexibly to support teaching and learning around cultural competence are not available across many European countries. The aim of the TransCoCon (2017-2020) project has been to develop innovative accessible multi-media learning resources to enable undergraduate nursing students and registered nurses in five countries to develop their cultural self-efficacy and cultural competence for nursing. The purpose of this paper is to describe and discuss this European ERASMUS + funded strategic partnership project (TransCoCon 2017-2020) and the creation of its underpinning theoretical and organising framework. The rationale for this guiding framework will be discussed within the context of supporting literature.
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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 | 0.052 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".