Self-directed learning in nursing education-What do the students do to learn nursing? Student perspective
Bibliographic record
Abstract
This article reports on a Danish research project investigating nursing students’ initiatives to learn nursing. There is an international focus on nursing students’ competencies to learn Self-Directed so that they can continue to develop their nursing competencies to provide patient-centered care and meet the demands of the ever-developing healthcare system. The aim was to investigate nursing students’ learning initiatives to learn nursing and to realize learning areas in which further support is required to develop students’ Self-Directed Learning ability. A phenomenological-hermeneutic approach was taken. The participants comprised a class of nursing students, who we followed throughout their 3.5-year Bachelor’s Degree Programme in Nursing. The data were generated by narrative interviews and a survey about students' learning initiatives. Three themes emerged: learning by preparing, learning by writing, and learning in interaction. Most students initiated learning activities based on their learning abilities, their life circumstances, and the learning resources available. The Self-Directed Learning ability varied among students, and a few needed external motivations and more supervision than they got to achieve competencies to learn self-directed. This study provides knowledge about nursing students’ self-directed learning initiatives and uncovers some areas to consider when planning to facilitate the development of Self-Directed Learning among nursing students. Faculty may consider how to allow more time for supervision and how to encourage nursing students’ motivation to develop Self-Directed Learning ability, so Self-Directed Learning ability can increase among all nursing students.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".