A Bibliometric Analysis of the 100 Most Cited Articles on Problem-based Learning in Medical Education
Bibliographic record
Abstract
Problem-based learning (PBL) is an instructional approach used in medical education that is characterized by solving patient-based problems in small groups with tutor guidance. More than 50 years since PBL’s inception, many questions remain to be addressed about its processes and learning outcomes. This study examined the bibliometric characteristics of the 100 most cited articles on PBL in medical education to identify the landmark papers that have made significant contributions to PBL research. A search was conducted in the Scopus database to identify articles on PBL in medical education. Tables of citation rankings, research type, foci, and network visualizations of collaborations between authors and countries were generated. The 100 most cited articles were contributed by 212 authors in 23 journals between 1981-2016. Most articles (68%) were published in Medical Education, Academic Medicine and Medical Teacher. The majority of the articles (71%) originated from Netherlands, Canada, and the United States and six prolific authors were identified. The articles cover a broad range of topics from the theoretical basis for PBL to its effectiveness. The strong author and country collaborative networks indicate continued global interest in the PBL instructional method. Our findings show a paucity of studies on the actual implementation of PBL, its long-term impact, as well as rigorous qualitative studies.
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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.224 | 0.239 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".