i-Assess: Evaluating the impact of electronic data capture for OSCE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Tablet-based assessments offer benefits over scannable-paper assessments; however, there is little known about the impact to the variability of assessment scores. METHODS: Two studies were conducted to evaluate changes in rating technology. Rating modality (paper vs tablets) was manipulated between candidates (Study 1) and within candidates (Study 2). Average scores were analyzed using repeated measures ANOVA, Cronbach's alpha and generalizability theory. Post-hoc analyses included a Rasch analysis and McDonald's omega. RESULTS: Study 1 revealed a main effect of modality (F (1,152) = 25.06, p < 0.01). Average tablet-based scores were higher, (3.39/5, 95% CI = 3.28 to 3.51), compared with average scan-sheet scores (3.00/5, 95% CI = 2.90 to 3.11). Study 2 also revealed a main effect of modality (F (1, 88) = 15.64, p < 0.01), however, the difference was reduced to 2% with higher scan-sheet scores (3.36, 95% CI = 3.30 to 3.42) compared with tablet scores (3.27, 95% CI = 3.21 to 3.33). Internal consistency (alpha and omega) remained high (>0.8) and inter-station reliability remained constant (0.3). Rasch analyses showed no relationship between station difficulty and rating modality. DISCUSSION: Analyses of average scores may be misleading without an understanding of internal consistency and overall reliability of scores. Although updating to tablet-based forms did not result in systematic variations in scores, routine analyses ensured accurate interpretation of the variability of assessment scores. CONCLUSION: This study demonstrates the importance of ongoing program evaluation and data analysis.
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.
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 | 0.016 | 0.529 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it