A systematic review of validity evidence for checklists versus global rating scales in simulation‐based assessment
Bibliographic record
Abstract
CONTEXT: The relative advantages and disadvantages of checklists and global rating scales (GRSs) have long been debated. To compare the merits of these scale types, we conducted a systematic review of the validity evidence for checklists and GRSs in the context of simulation-based assessment of health professionals. METHODS: We conducted a systematic review of multiple databases including MEDLINE, EMBASE and Scopus to February 2013. We selected studies that used both a GRS and checklist in the simulation-based assessment of health professionals. Reviewers working in duplicate evaluated five domains of validity evidence, including correlation between scales and reliability. We collected information about raters, instrument characteristics, assessment context, and task. We pooled reliability and correlation coefficients using random-effects meta-analysis. RESULTS: We found 45 studies that used a checklist and GRS in simulation-based assessment. All studies included physicians or physicians in training; one study also included nurse anaesthetists. Topics of assessment included open and laparoscopic surgery (n = 22), endoscopy (n = 8), resuscitation (n = 7) and anaesthesiology (n = 4). The pooled GRS-checklist correlation was 0.76 (95% confidence interval [CI] 0.69-0.81, n = 16 studies). Inter-rater reliability was similar between scales (GRS 0.78, 95% CI 0.71-0.83, n = 23; checklist 0.81, 95% CI 0.75-0.85, n = 21), whereas GRS inter-item reliabilities (0.92, 95% CI 0.84-0.95, n = 6) and inter-station reliabilities (0.80, 95% CI 0.73-0.85, n = 10) were higher than those for checklists (0.66, 95% CI 0-0.84, n = 4 and 0.69, 95% CI 0.56-0.77, n = 10, respectively). Content evidence for GRSs usually referenced previously reported instruments (n = 33), whereas content evidence for checklists usually described expert consensus (n = 26). Checklists and GRSs usually had similar evidence for relations to other variables. CONCLUSIONS: Checklist inter-rater reliability and trainee discrimination were more favourable than suggested in earlier work, but each task requires a separate checklist. Compared with the checklist, the GRS has higher average inter-item and inter-station reliability, can be used across multiple tasks, and may better capture nuanced elements of expertise.
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 | 0.108 | 0.401 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.022 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".