Including administrators in curricular redesign: How the academic–practice relationship can bridge the practice–theory gap
Bibliographic record
Abstract
AIM: Health care administrators provided information through semi-structured interviews as to how one faculty of nursing (FoN) was preparing students for practice. BACKGROUND: There is a long-standing disconnect between the nursing education and the clinical arena known as the theory-practice gap. The FoN wanted to redevelop their curriculum to better prepare students for practice and bridge the gap. METHOD: Using developmental evaluation, 36 administrators were interviewed and asked about their expectations of newly graduated nurses, the FoN curriculum, and changes to be made. RESULTS: Four themes were identified: entry to programme; curricular content, delivery and structure; clinical recommendations; and stronger relationships. CONCLUSION: Strong academic-practice partnerships are still needed. The current lack of communication and partnership has compromised students' quality of education and their transition into the workforce. IMPLICATIONS FOR NURSING MANAGEMENT: Leaders in both the education and practice settings can better prepare newly graduated nurses and bridge the theory-practice gap by co-creating a joint committee and creating more touchpoints with one another. A joint committee can develop appropriate entry-to-programme guidelines, discuss relevant trends in practice and shape the curriculum. Clinical experiences for students may also act as extra touchpoints whereby the two groups can discuss clinical mentorship needs and build stronger academic-practice relationships.
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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 | 0.117 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.014 |
| Scholarly communication | 0.024 | 0.016 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".