Bibliographic record
Abstract
Background: Portfolios are used in medical practice as a means of instilling “reflective learning” in doctors and accumulating evidence of the doctor’s competence. It is a mandatory requirement by the General Medical Council (GMC), as a form of public accountability, for licensed clinicians to maintain an e-portfolio of daily clinical practice, which is subjected to annual appraisals and ultimately influences their ability to renew their license to practise in the UK. This article reviews the reflective learning process for which the e-portfolio is intended to instil in doctors and the level of evidence required to demonstrate competency and continuing professional development.Methods: A literature review was conducted on Medline and Google Scholar for any available guidance on writing e-portfolio entries and guidelines from the GMC, Royal Colleges and various training boards were reviewed to determine the type of evidence required to be demonstrated.Results: Fifteen articles had met the inclusion criteria on guiding e-portfolio writing. Guidelines reviewed constantly echoed the theme of “reflecting doctors” and “linking evidence to curriculum outcomes”. This article has also proposed a “Do, Reflect, Plan, Act” framework in writing portfolio entries.Conclusions: Creating and maintaining an e-portfolio throughout a lifelong career is no mean feat. We have reviewed the key components that clinicians ought to demonstrate in their e-portfolios, and introduced the “Do, Reflect, Plan, Act” framework, to enhance understanding of the e-portfolio as a learning tool to improve medical practice.
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 | 0.051 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.025 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".