A Concept Analysis of Cultural Appreciation in Addressing the Wholistic Health Needs of Indigenous People
Bibliographic record
Abstract
Aim: Cultural appreciation is found within the arts, psychology, counseling, health disciplines, and education. Currently, in the literature, there is not a strong link between cultural appreciation, nursing, and Indigenous people. The aim of this concept analysis is to analyze the concept of cultural appreciation for nurse educators, nurse researchers, and nurse leaders to apply to culturally appreciate Indigenous people within their geographical areas which can result in meeting their wholistic care needs. Design: This concept analysis of cultural appreciation uses Walker & Avant’s (2019) approach to define cultural appreciation, antecedents, empirical referents, and consequences. Results: The antecedents of cultural appreciation are cultural appropriation, oppression, cultural prejudice, privilege, and lack of knowledge to integrate the wholistic health of Indigenous people into practice, education, and research. The defining attributes of cultural appreciation are awareness, knowledge acquisition, and desire. The consequence of cultural appreciation is wholistic care of Indigenous people as defined by their ways of knowing and being. Conclusion: The concept analysis of cultural appreciation integrates Indigenous wholistic health beliefs and ways of knowing and being that can advance holistic nursing knowledge for nurses, educators, and researchers.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".