“Undergraduate students’ perceptions of learning nursing theories: A descriptive qualitative approach”
Bibliographic record
Abstract
AIM: This study examined undergraduate students' perceptions of learning nursing theories and the contribution of these theories to clinical practice. BACKGROUND: Nursing theories are the foundation of the discipline. Students' perceptions of learning nursing theories are under-investigated. DESIGN: This descriptive study used a qualitative approach with five questions survey and group narratives. METHODS: 163 first-year nursing students (female= 85%) participated in the study. Participants chose the best-fit theory to answer individually questions on the contribution of six learnt theories (McGill Model of Nursing, Self-Care Deficit Nursing Theory, Theory of Humanbecoming, Theory of Interpersonal Relations, Adaptation Model of Nursing and Theory of Human Caring) to their clinical practice. They discussed their answers in groups and provided group narratives. RESULTS: Responses of 163 participants showed no theory to be predominant. Narratives' analysis revealed four themes: Pluralism in the view of nursing theories, Dualism in the view of nursing practice, Monism in the view of the person and Learning based on personal values and social context. CONCLUSIONS: Students recognize the plurality of theories and the Person holistically. Teaching nursing theory in the undergraduate program should support the use of theoretical knowledge relevant to practice and promote its direct application during clinical training. TWEETABLE ABSTRACT: This study examined undergraduate students' perceptions of learning nursing theories using a qualitative approach. Narratives revealed Pluralism in the view of theories, Dualism in the view of practice, Monism in the view of the person and Learning based on personal values and social context.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".