Educational interventions to improve ergonomics in gastrointestinal endoscopy: a systematic review
Bibliographic record
Abstract
Abstract Background and study aims Endoscopists are at high risk of musculoskeletal pain and injuries (MSPI). Recently, ergonomics has emerged as an area of interest to reduce and prevent the incidence of MSPI in endoscopy. The aim of this systematic review was to determine educational interventions using ergonomic strategies that target reduction of endoscopist MSPI from gastrointestinal endoscopy. Methods In December 2020, we conducted a systematic search in MEDLINE, EMBASE, PsycINFO, Web of Science, Scopus, the Cochrane Central Register of Controlled Trials and the Cochrane Database of Systematic Reviews for articles published from inception to December 16, 2020. Studies were included if they investigated educational interventions aimed at changing knowledge and/or behaviors related to ergonomics in gastrointestinal endoscopy. After screening and full-text review, we extracted data on study design, participants, type of training, and assessment of primary outcomes. We evaluated study quality with the Medical Education Research Study Quality Instrument (MERSQI). Results Of the initial 575 records identified in the search, five met inclusion criteria for qualitative synthesis. We found that most studies (n = 4/5, 80 %) were single-arm interventional studies that were conducted in simulated and/or clinical settings. The most common types of interventions were didactic sessions and/or videos (n = 4/5, 80%). Two (40 %) studies used both standardized assessment studies and formal statistical analyses. The mean MERSQI score was 9.7. Conclusions There is emerging literature demonstrating the effectiveness of interventions to improve ergonomics in gastrointestinal endoscopy.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".