How ready are nursing colleges to be integrated into higher education? A scoping review of influential factors
Bibliographic record
Abstract
Background and aim: The integration of college-based nursing education into higher education has gained significant momentum worldwide. However, in countries where integration has not happened some nursing colleges continue to encounter challenges in their readiness for this process. The study aimed to identify and map the breadth of evidence available on the factors that influence the readiness of nursing colleges to transition into higher education.Methods: Databases such as PubMed, CINAHL, Health Source Nursing Academic Edition, SCOPUS, Google scholar, and Educational Resources Information Centre were used. The review focused on literature published only in English from 1996 to 2021. Arksey and O’Malley’s scoping review framework was used.Results: The search identified 1,408 publications; 23 of these met the inclusion criteria and were selected for full-text review. The following themes emerged: regulations and policies, recognition of nursing education, nurse educator roles, and financial considerations.Conclusions: The review’s findings revealed a need for clear policy frameworks to guide higher education integration and regulatory processes for nursing colleges. Becoming part of higher education improves nursing’s academic status, however, integration may give rise to challenges associated with the lack of educator involvement in policy development and integration plans, and unchanged funding arrangements. The repositioning of nursing colleges should take cognisance of lessons from other countries regarding integration readiness to ensure that change happens with minimal disruption and disharmony.
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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 | 0.042 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.028 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".