A reflexive exercise to promote cultural humility among doctoral nursing students
Bibliographic record
Abstract
Background: The U.S. population is becoming increasingly diverse; however, nursing remains a predominantly Caucasian profession. To promote positive outcomes among diverse patients, nurses must rely on rigorous transcultural research. When conducting research with people different from oneself, knowledge of one’s own values and beliefs is a necessary first step. In Transcultural Nursing Research, a required doctoral course, the first assignment is a reflexive exercise followed by online discussion about one’s personal culture and the origins of values and beliefs. Objectives: To (a) examine students’ responses to a reflexive exercise for evidence of cultural self-awareness, cultural humility, and insights gained and (b) assess the effectiveness of the teaching method.Methods: The setting was online within the Learning Management System (LMS), Blackboard. Participants: The sample consisted of twelve doctoral students enrolled in Transcultural Nursing Research. Methods: Student consent was obtained after course grades were submitted. Data was extracted from the LMS, de-identified, coded, categorized and collapsed into themes.Results: Four themes emerged from the data: “different versus familiar”, “cultural experiences”, “memories” and “reflections and implications”.Conclusions: Posts reflected insight into cultural awareness, values and humility. Students identified growth opportunities for themselves and their children. Suggestions for future education and research are presented.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".