Nursing students' experiences of moral uncertainty in the context of global health practicums
Bibliographic record
Abstract
Background: Increasingly, undergraduate nursing students are electing to participate in global health practicums (GHPs). However, empirical and theoretical nursing literature that is dedicated to exploring the ethical implications of GHPs in nursing is surprisingly nascent. Research Design: The aim of this interpretive descriptive study was to explore nursing students’ experiences of moral uncertainty in the context of GHPs. Data was collected using semi-structured interviews with 17 graduates of an undergraduate nursing program in Western Canada who had participated in a GHP in their final semester of nursing school. Findings: Four main themes typifying the data were constructed. In the first theme, heightened reality in the global context, experiencing the ‘exotic’ nature of working in a foreign environment was a source of considerable moral uncertainty for past GHP participants. Moral uncertainty was born out of participants’ grappling with issues related to heightened positions of power, influence, and visibility. In the second theme, cultural collisions in healthcare, moral uncertainty, and at times, moral distress, was derived from experiencing alternative approaches to care, and in particular, what participants perceived as a quelled, less action-oriented approach to care. In the third theme, blurring of boundaries: Scope of practice issues, practicing beyond one’s scope of practice contributed substantially to participants’ experiences of moral uncertainty. Lastly, in the fourth theme, connectedness, a sense of belonging was of paramount importance in helping participants to redress experiences of moral uncertainty. Discussion: This study’s findings indicate that nursing students from one Canadian university experienced a considerable amount of moral uncertainty, and less frequently, moral distress, during their GHP experience. Underlying much of the moral uncertainty was a misalignment between what participants expected their GHP to entail, and what was experienced in reality. Participants reflected deeply on these disparities, evidencing the development of a critical approach to global citizenship, as well as a heightened social consciousness. However, further integration of critical and transformative pedagogies into pre-departure training for GHPs, specifically, and undergraduate nursing education, more broadly, is needed.
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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 | 0.018 |
| 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 | 0.018 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| 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".