Nursing students' experiences of moral uncertainty in the context of global health practicums
Bibliographic record
Abstract
More students than ever are electing to take part in international practicums from health-related disciplines. With the goal of better understanding the moral experiences and ethical implications of global health practicums (GHPs), the purpose of this Interpretive Descriptive study was to examine the moral uncertainty of nursing students from one university in Canada. Seventeen nurses who had participated in a GHP in their undergraduate nursing program participated in semi-structured interviews. Data were analyzed inductively using constant comparative data analysis techniques, and a thematic account of participants' experiences was developed. Findings suggest that nursing students experienced considerable amounts of moral uncertainty during their GHP. Most often, participants' experiences of uncertainty stemmed from a misalignment between their expectations and reality, including encountering different approaches to healthcare, being situated in new cultural and clinical care environments, and grappling with how best to stay within one's scope of student professional practice. Participants inconsistently reflected on these experiences, which may present a missed opportunity for professional growth through the development of a heightened social consciousness. Educators can facilitate this process by implementing robust predeparture training for GHPs, clarifying program objectives, and providing clinical debriefing.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".