Using Portfolios in Medical Education: A Content Analysis of Presentations at the Association for Medical Education in Europe Conferences 2009-2011
Bibliographic record
Abstract
Background: Although a few studies have explored the use of portfolios in undergraduate and postgraduate medical education, there is a need for an up-to-date review of research on portfolio use in medical education according to sources that reflect recent developments from researchers with a variety of cultural backgrounds. Methods: We conducted content analysis of research on portfolios presented at the annual Conferences of the Association for Medical Education in Europe (AMEE) between 2009 and 2011. We identified 92 presentations addressing portfolio use in medical education and analysed the abstracts of these presentations in terms of authors' nationality, research participants and key themes. Results: The number of presentations addressing the use of portfolios in medical education has increased. Authors from English-speaking countries, including the United Kingdom, United States, and Canada, contributed most of the portfolio abstracts. However, researchers from non-western countries, such as Thailand, Japan, Saudi Arabia, and South Africa have gradually joined the forum. Most studies described the use of portfolios in undergraduate and postgraduate medical education. Major themes were categorized by the roles medical educators play in the application of portfolios in medical education. Educators generally served as evaluators, developers, implementers, trainers, and investigators. Conclusions: The geographic expansion of portfolio use was observed. Positive outcomes of portfolio use in stimulating reflection and self-awareness are consistent with the findings of previous reviews. Time consumption, negative attitudes, and misconceptions regarding portfolios remain as barriers to their implementation. Further research to explore the adaptation of portfolios in diverse cultures, the application of portfolios in continuing professional development, and the incorporation of new electronic appliances are suggested.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".