Identification of nursing assessment models/tools validated in clinical practice for use with diverse ethno-cultural groups: an integrative review of the literature
Bibliographic record
Abstract
BACKGROUND: High income nations are currently exhibiting increasing ethno-cultural diversity which may present challenges for nursing practice. We performed an integrative review of literature published in North America and Europe between 1990 and 2007, to map the state of knowledge and to identify nursing assessment tools/models which are have an associated research or empirical perspective in relation to ethno-cultural dimensions of nursing care. METHODS: Data was retrieved from a wide variety of sources, including key electronic bibliographic databases covering research in biomedical fields, nursing and allied health, and culture, e.g. CINAHL, MEDline, PUBmed, Cochrane library, PsycINFO, Web of Science, and HAPI. We used the Critical Appraisal Skills Programme tools for quality assessment. We applied Torraco's definition and method of an integrative review that aims to create new knowledge and perspectives on a given phenomena. To add methodological rigor with respect to the search strategy and other key review components we also used the principles established by the Centre for Reviews and Dissemination. RESULTS: Thirteen thousand and thirteen articles were retrieved, from which 53 full papers were assessed for inclusion. Eight papers met the inclusion criteria, describing research on a total of eight ethno-cultural assessment tools/models. The tools/models are described and synthesized. CONCLUSIONS: While many ethno-cultural assessment tools exist to guide nursing practice, few are informed by research perspectives. An increased focus on the efficiency and effectiveness of health services, patient safety, and risk management, means that provision of culturally responsive and competent health services will inevitably become paramount.
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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 | 0.098 | 0.249 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.045 | 0.033 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 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".