Evaluating fundamental care knowledge to inform educational leadership
Bibliographic record
Abstract
AIM: To explore undergraduate nursing students' understanding of fundamental care and identify educational leadership opportunities to deepen students' understanding of fundamental care concepts. DESIGN: Sequential-explanatory mixed methods study. METHODS: We conducted a cross sectional survey (n = 202) and focus groups (n = 24) to explore undergraduate nursing students' ability to identify fundamental care needs. All data were collected between November 2020 and April 2021. Survey data were analysed using descriptive and inferential statistics and focus group data were thematically analysed. RESULTS: Year One students scored significantly lower in their ability to identify fundamental care needs compared with students in other years, even after controlling for route, gender and age. Post-degree students scored significantly higher than direct entry or transfer students. Students ≤19 years of age had significantly lower scores compared with students ≥25 years of age. Our focus group findings highlighted that students were often unable to define fundamental care, but they identified learning about various components of fundamental care in a variety of ways. While students understood that fundamental care was required in all settings, they were challenged in providing this care in acute and virtual settings. Students shared several suggestions to support fundamental care skills development across the curriculum. CONCLUSIONS: There is a need for a clear definition and description of the fundamentals of care that is used consistently by faculty, students and curriculum documents. It is important to encourage and support educators to share real-world nursing stories, offer students time to share their personal experiences, develop creative learning opportunities and foster student reflection to deepen students' understanding of the fundamentals of care. IMPACT: Educators need support to meaningfully incorporate fundamentals of care learning opportunities across multiple care settings. Educational leaders can use these findings to develop or adapt their curricula to support fundamental care skill development.
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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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".