Decay of Competence with Extended Research Absences During Residency Training: A Scoping Review
Bibliographic record
Abstract
A significant number of residents in postgraduate training programs pursue dedicated research training. Currently, no formal curricula exist to transition residents back into clinical roles following dedicated research leave. This scoping review aims to determine what literature exists on the challenges faced by trainees who interrupt their clinical training for extended periods of time for research leave. The Pubmed and Medline databases were searched for all study designs related to postgraduate trainees taking academic or research leave. A three-step selection process including title, abstract and full-article review was employed to identify articles that mentioned decay of knowledge, skill or competence. A narrative review of the literature was generated to present key themes identified within the studies. The search yielded 174 articles of which five investigated resident skill decay during research leave. The five studies included for analysis were cohort studies that used general surgery residents' self-perception and faculty members' perception of residents' skill decay as a measure. Residents and faculty perceived decay of residents' technical skills, leadership skills and knowledge following dedicated research leave. The greatest decay perceived was in technical skills, specifically with more complex tasks and longer periods of non-use. This review identified that residents and faculty perceive a decay of resident skills following dedicated research training. To provide the necessary support to limit this potential decay, as well as to assist in the transition back into clinical training, the needs of and challenges faced by research residents and postgraduate programs must be better understood.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".