DOPS assessment: A study to evaluate the experience and opinions of trainees and assessors
Bibliographic record
Abstract
BACKGROUND: Workplace based assessments (WBAs) have been part of UK training for the last 3 years. Carrying out procedures efficiently and safely is of paramount importance in anaesthesia. AIMS: To explore opinions and experiences of Direct Observation of Procedural Skills (DOPS) assessments in a regional anaesthetic training programme. METHODS: 19 and 20-item questionnaires were distributed to trainees and consultants respectively. RESULTS: Questionnaire response rate was 76% (90/119) for trainees and 65% (129/199) for consultants. 43% of consultants and 33% of trainees were not trained in DOPS use. Assessments were usually not planned. 50% were ad hoc and the remainder mainly retrospective. Time spent on assessment was short with DOPS and feedback achieved in ≤15 minutes in the majority of cases with lack of suggestions for further improvement. Both trainees and consultants felt that DOPS was not a helpful learning tool (p = 0.001) or a reflection of trainee competency. CONCLUSIONS: DOPS assessments are currently not valued as an educational tool. Training is essential in use of this WBA tool which needs to be planned and sufficient time allocated so as to address current negative attitudes.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".