Developing culturally competent and compassionate healthcare leaders: A European model
Bibliographic record
Abstract
Objective: This paper aims to describe the development of a European model that refers for healthcare leadership. The model promotes the values of cultural competence and compassion.Methods: The development of this model is part of the IENE 4 EU funded project with participating countries: United Kingdom, Spain, Cyprus, Romania, Italy, Denmark, Turkey. Its development is based on a) a needs assessment survey among healthcare leaders in the partner countries (N = 199), b) two round Delphi study with 14 experts and c) a focus group with healthcare leaders after the development of the model.Results: The components of this model include the basic principles, values and skills that a health care leader should have as a role model and a coach of his/her staff in delivering compassionate and culturally competent care. This model was further used within the IENE 4 project, as a tool for creating learning tools, aiming to improve the quality of care within a cultural framework. Fourteen such learning tools were developed and piloted in all partner countries.Conclusions: Health care leaders need to guide, mentor and support their staff and collaborate among them and with patients and families, as to provide quality care within a safe, compassionate and culturally appropriate environment. This model highlights the key principles of culturally competent and compassionate health care leadership.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".