Exploring why and how encounters with the Norwegian health-care system can be considered culturally unsafe by North Sami-speaking patients and relatives: A qualitative study based on 11 interviews
Bibliographic record
Abstract
BACKGROUND: Citizens of Norway have free and equal access to healthcare. Nurses are expected to be culturally sensitive and have cultural knowledge in encounters with patients. Culturally safe care is considered both a process and an outcome, evaluated by whether the patients feel safe, empowered and cared for, or not. All patients request equal access to quality care in Norway, also Sami patients. OBJECTIVES: The aim of the study is to identify whether Sami patients and relatives feel culturally safe in encounters with healthcare, and if not, what are the main concerns. METHODS: This qualitative study used semi-structured interviews in the North Sami language, with 11 North Sami participants.The transcribed data were analysed through a lens of cultural safety by content analysis. FINDINGS: Data analysis explicated themes including: use of Sami language, Sami identity and cultural practices, connections to positive health outcomes to enhance cultural safe care and well-being for North-Sami people encountering the Norwegian health-care system. CONCLUSION: Culturally safe practices at the institutional, group and individual levels are essential to the well-being of Sami people. An engagement in culturally safe practices will facilitate (or) fulfil political and jurisdictional promises made to the Sami people, consequently improving positive impact of healthcare.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".