Profiling postgraduate workplace-based assessment implementation in Ireland: a retrospective cohort study
Bibliographic record
Abstract
In 2010, workplace-based assessment (WBA) was formally integrated as a method of formative trainee assessment into 29 basic and higher specialist medical training (BST/HST) programmes in six postgraduate training bodies in Ireland. The aim of this study is to explore how WBA is being implemented and to examine if WBA is being used formatively as originally intended. A retrospective cohort study was conducted and approved by the institution's Research Ethics Committee. A profile of WBA requirements was obtained from 29 training programme curricula. A data extraction tool was developed to extract anonymous data, including written feedback and timing of assessments, from Year 1 and 2 trainee ePortfolios in 2012-2013. Data were independently quality assessed and compared to the reference standard number of assessments mandated annually where relevant. All 29 training programmes mandated the inclusion of at least one case-based discussion (max = 5; range 1-5). All except two non-clinical programmes (93 %) required at least two mini-Clinical Evaluation Exercise assessments per year and Direct Observation of Procedural Skills assessments were mandated in 27 training programmes over the course of the programme. WBA data were extracted from 50 % of randomly selected BST ePortfolios in four programmes (n = 142) and 70 % of HST ePortfolios (n = 115) in 21 programmes registered for 2012-2013. Four programmes did not have an eligible trainee for that academic year. In total, 1142 WBAs were analysed. A total of 164 trainees (63.8 %) had completed at least one WBA. The average number of WBAs completed by HST trainees was 7.75 (SD 5.8; 95 % CI 6.5-8.9; range 1-34). BST trainees completed an average of 6.1 assessments (SD 9.3; 95 % CI 4.01-8.19; range 1-76). Feedback-of varied length and quality-was provided on 44.9 % of assessments. The majority of WBAs were completed in the second half of the year. There is significant heterogeneity with respect to the frequency and quality of feedback provided during WBAs. The completion of WBAs later in the year may limit available time for feedback, performance improvement and re-evaluation. This study sets the scene for further work to explore the value of formative assessment in postgraduate medical education.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".