146 Undergraduate nursing students’ experiences with supporting patients with difficult health decisions
Bibliographic record
Abstract
Introduction There are few opportunities in undergraduate nursing curriculum for students to acquire knowledge and skills in shared decision-making (SDM). Through a theory-based learning assignment, we aimed to explore how undergraduate nursing students understand and participate in SDM in their clinical placements. Methods Descriptive analysis of an undergraduate nursing learning assignment. Upon completion of the Ottawa Decision Support Tutorial, undergraduate nursing students posted a virtual reflective note about a clinical experience in which they were involved in a patient’s difficult health decision. From these reflections, we inductively identified decisional needs, interventions, and outcomes, then mapped them onto the Ottawa Decision Support Framework (ODSF). We used content analysis to synthesize the factors influencing nursing students’ participation in SDM. Results Preliminary results from 34 reflective notes showed that nursing students are most often involved in difficult medical/surgical and end-of-life decisions with their patients. Frequent decisional needs included inadequate knowledge (n=21;61.8%), and complex decisional characteristics (n=20;58.8%). Sixteen (47.1%) students raised personal and clinical characteristics as decisional needs;12 of which (75%) did not explicitly identify them as decisional needs. Decision aids were the most cited intervention (n=27;79.4%). Quality decision was commonly defined as values-based and informed, yet being informed was prioritized over personal values. Factors influencing nursing student participation in SDM included decision support knowledge, interpersonal skills, ensuring accessibility of decision aids for diverse populations (e.g., cultures), and within specific contexts (e.g., end-of-life). Discussion As health systems are increasingly seeking ways to engage patients/families in decision- making, every effort should be made to equip future nurses with decision support knowledge, skills, and resources to support its integration into practice. Conclusion A theory-based learning assignment permitted undergraduate nursing students to identify difficult health decisions, decisional needs, interventions to overcome them, and outcomes. Greater curricular focus is required to prepare nursing students for SDM.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".