Audit to investigate junior doctors' knowledge of how to administer and score the Montreal Cognitive Assessment (MoCA).
Bibliographic record
Abstract
AIM: To investigate junior doctors' knowledge of how to conduct the Montreal Cognitive Assessment (MoCA). METHODS: A two-part questionnaire was administered to junior doctors at teaching sessions across three New Zealand district health boards. Part 1 investigated prior experience and knowledge of the MoCA. Part 2 tested junior doctors' ability to identify errors in administration and how to score the test. Several weeks later a brief MoCA teaching session was given followed immediately by a repeat questionnaire. RESULTS: Seventy-one individuals completed the initial audit and 46 did the follow-up audit. The majority of junior doctors carried out the MoCA on a monthly basis. Prior to our teaching session, only 23% of participants had received formal teaching on how to administer and score the MoCA. The majority (89%) of participants thought that the teaching session had improved their ability to conduct the MoCA. Statistically significant changes were seen in participants' ability to administer the trail-making question and to score the example questions of clock faces, naming animals, serial seven subtractions, verbal fluency testing, abstraction and the awareness about the effect of years of education on the MoCA score. CONCLUSION: Junior doctors administer and score the MoCA but many have not received formal teaching on how to do so. A short teaching session improved their ability to conduct the MoCA and identify errors in administration and scoring.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".