Application of Leininger's Theory of Transcultural Nursing into Practice
Bibliographic record
Abstract
The purpose of the poster was to demonstrate how Leininger's theory of transcultural nursing is utilized daily within the multicultural pediatric population at the hospital for sick children. Methods: Within this diverse and challenging population, the hemodialysis nurse is constantly challenged to ensure that the nursing care provided to the patient is reflective and meeting the needs of our pediatric population and their families. Leininger's theory of transcultural nursing has provided an avenue to ensure that the many cultures of our clients are maintained and respected. A case study will be used to show the integration of Leininger's theory. Resources for supporting the cultural values of the hemodialysis patient will also be identified. Conclusion: The use of a transcultural nursing model enhances the quality and effectiveness of the patient care provider for the pediatric hemodialysis patient at the hospital for sick children. Utilization of a transcultural nursing model ensures that the patients and their families are respected and supported for their diverse backgrounds by the hemodialysis nurse. The hemodialysis nurses are providing quality nursing care which reflects the needs and concerns of a multicultural population.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".