Managing residents in difficulty within CBME residency educational systems: a scoping review
Bibliographic record
Abstract
BACKGROUND: Best practices in managing residents in difficulty (RID) in the era of competency-based medical education (CBME) are not well described. This scoping review aimed to inventory the current literature and identify major themes in the articles that address or employ CBME as part of the identification and remediation of residents in difficulty. METHODS: Articles published between 2011 to 2017 were included if they were about postgraduate medical education, RID, and offered information to inform the structure and/or processes of CBME. All three reviewers performed a primary screening, followed by a secondary screening of abstracts of the chosen articles, and then a final comprehensive sub-analysis of the 11 articles identified as using a CBME framework. RESULTS: Of 165 articles initially identified, 92 qualified for secondary screening; the 63 remaining articles underwent full-text abstracting. Ten themes were identified from the content analysis with "identification of RID" (41%) and "defining and classifying deficiencies" (30%) being the most frequent. In the CBME article sub-analysis, the most frequent themes were: need to identify RID (64%), improving assessment tools (45%), and roles and responsibilities of players involved in remediation (27%). Almost half of the CBME articles were published in 2016-2017. CONCLUSIONS: Although CBME programs have been implemented for many years, articles have only recently begun specifically addressing RID within a competency framework. Much work is needed to describe the sequenced progression, tailored learning experiences, and competency-focused instruction. Finally, future research should focus on the outcomes of remediation in CBME programs.
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 | 0.003 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".