The Multiple Errands Test: A Guide for Site-Specific Version Development
Bibliographic record
Abstract
Background. The complex and real-world nature of the Multiple Errands Test (MET) makes it a valuable and increasingly popular assessment of cognitive function. However, these same qualities make its local implementation challenging. Purpose. To produce an evidence-based guide for site-specific adaptation of the MET. Method. The CAN-IMPLEMENT © knowledge translation framework informed a structured approach to the creation of a guide to site-specific version development, informed by twenty-two published approaches to MET adaptation. Applicability of the guide was supported by a two-phase revision process, in which a site-specific hospital and community version produced from its recommendations were administered with forty-two neurologically intact participants and stakeholder feedback obtained. Findings. We offer an outline of core components which maintain the integrity of the MET, and adaptable peripheries which may be modified when required by the local setting. Implications. The proposed guide provides a systematic yet flexible guide for site-specific MET development.
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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 | 0.094 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.021 |
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".