Adoption of e-portfolios for Registered Nurses & Midwives Professional Registration and Revalidation in Ireland
Bibliographic record
Abstract
In many countries, such as Australia, Canada, the United Kingdom (UK), and the United States of America, Nursing and Midwifery Professional Bodies undergo a revalidation process demonstrating evidence of continuing professional development (CPD) and clinical practice hours in order to remain an active member on the professional register. In most countries this process involves documenting evidence in paper format. However, in the UK, our closest neighbour, eportfolio submission is now offered as an option for revalidation. Ireland is imminently moving towards introducing documented evidence as a requirement for continued registration as currently, there are no requirements to demonstrate evidence of continuing professional development or clinical practice as part of the annual re-registration process. While there are plans to address this in the near future, there are no details regarding the revalidation requirements or process. Irish undergraduate and postgraduate nursing and midwifery students are beginning to use eportfolios for assessment purposes and are increasingly familiar with the online eportfolio platforms. In this paper, we provide a rationale for the Irish Nursing and Midwifery Board (NMBI) to adopt eportfolios for the submission of documentary evidence for both initial registration and revalidation. We will examine the advantages and the barriers to the introduction of eportfolios in this context. The use of eportfolios would provide the NMBI an opportunity to lead the way in registration and revalidation processes internationally, enabling nurses and midwives in Ireland to embrace the opportunities that the digital age presents.
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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 | 0.139 | 0.205 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".