[Development and validation of a uniform QUality Instrument for ClerKship (QUICK)].
Bibliographic record
Abstract
OBJECTIVE: Because both clerks and medical faculty quality management workers expressed the need for it, we aimed to develop a compact, valid and uniform instrument to assess the quality of Dutch clinical clerkships across all medical faculties in the Netherlands. METHOD: We divided all 249 items from existing published and unpublished clerkship quality instruments into the three essential learning environment domains: content, atmosphere and organisation. In a 3-stage Delphi procedure, the 45 most relevant items from this list were selected that comprehensively covered the three domains. All clinical clerks in the country's northeastern educational region were invited to evaluate their last clerkship using this draft instrument. We used half of these data for item reduction and the other half to validate the final instrument, the QUality Instrument for ClerKships (QUICK). RESULTS: After the Delphi procedure and further item reduction, the QUICK comprises 15 items, 5 in each domain. The internal consistency of the QUICK and each of the three domains was satisfactory (Cronbach's α 0.88, 0.73, 0.84 and 0.67, respectively). The variance of the draft instrument domain scores were explained for >80% by item variance of the final QUICK. A panel of educational experts and medical faculty quality management workers evaluated QUICK's face validity as good. CONCLUSION: The QUICK is a concise and valid instrument to assess the quality of Dutch clinical clerkships. Its repeated use in a quality cycle can contribute to monitoring and ongoing development of the quality of this key phase in the medical education curriculum.
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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 | 0.045 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".