Human rights education for nurses: An example from Finland
Bibliographic record
Abstract
Background and objective: Nurses deal with complex human rights issues arising from difficult situations and ethical dilemmas involving patients, relatives, and health care professionals. Human rights education can enable nurses to understand principles of human rights and apply them at work in their efforts to provide high quality care. The objective for this study was to describe how human rights material was integrated into a professional ethics course for master degree nursing students and to facilitate nurse educators’ efforts to include such material in their courses.Methods: In this qualitative study, data consisted of responses to a human rights assignment by 23 nursing students at a university of applied sciences in Finland. Thematic analysis was used to identify patterns and themes from the assignment.Results: Participants’ consensus was that human rights education should be part of nursing curricula. Students described what they learned, identified similarities and differences between human rights principles and ethical codes, gave examples applying human rights principles to their work, and stated how they could better protect human rights of nurses and their patients.Conclusions: Learning about human rights reinforces nurses’ knowledge and application of ethical codes and increases their awareness of factors necessary for quality care.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| 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".