Issues in developing an educational and professional portfolio
Bibliographic record
Abstract
Portfolios have been widely used for many years in a variety of disciplines to demonstrate individual progression and development. These have also become increasingly popular in nursing and healthcare as a means of recording and enhancing learning, analyzing the integration of theory with practice, developing critical thinking and promoting personal professional development (Jasper and Rosser 2013; Byrne et al. 2007; Timmins and Dunne 2009; Hill 2012; Nursing and Midwifery Council (NMC) 2015a, 2015b). Nurses are under a professional obligation to ensure that their knowledge and skills are safe, current and effective (NMC 2015a), and this requires them to engage in appropriate learning and practice-oriented activities that develop and maintain competence and performance (Timmins and Dunne 2009; NMC 2015b). Thus as educational courses move toward more competency-based assessments, so the portfolio has come to play a vital role in making the process and development of learning transparent through its components, including reflective writing (Jasper and Mooney 2013; Jasper and Rosser 2013; Hill 2012; Green et al. 2014; NMC 2015b). Such an approach is based on andragogical principles whereby the learner takes responsibility for the scope and shape of their learning. The learner is recognized as being in control of his or her learning, contributing personal knowledge and experience to the process (Knowles 1990). Thus nurses are responsible for directing their own learning experiences and providing evidence of their competence. This demands a degree of self-directed learning, with the portfolio being used as a dynamic, flexible, highly individualized document of the learner’s development (Timmins and Dunne 2009; Garrett et al. 2013; Green et al. 2014; NMC 2015b).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.093 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.026 | 0.025 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".