Developing and Validating a Big-Store Multiple Errands Test
Bibliographic record
Abstract
The Multiple Errands Test (MET) is an ecologically valid assessment that characterizes how executive dysfunction manifests in everyday activities. Due to the naturalistic nature of this assessment, clinicians and researchers have had to develop site-specific versions resulting in numerous published versions and making it difficult to establish standard psychometric properties. The aim of this study was to develop a standardized, community version of the MET designed to be used in large department stores meeting set criteria that would not require site specific modifications. This paper reports on the development, content validity, feasibility, and inter-rater reliability of a Big-Store MET, and the performance of healthy participants on this test. Items were selected to match previously published versions in relation to quantity and complexity. Content validity was established by having experts (n=4) on the MET review the proposed Big-Store version and evaluate the task consistency with previously published versions. To assess feasibility of administration, and inter-rater reliability, a convenience sample of 14 community dwelling adults, self-reporting as healthy, were assessed by two trained raters. We found the Big-Store MET to be feasible to deliver (completed within 30 minutes, scores show variability, acceptable to participants in community environment) and inter-rater reliability to be very high (ICCs =0.92-0.99) with the exception of frequency of strategy use. This study introduces the Big-Store MET to the literature, establishes its preliminary validity and reliability thus laying the foundation for a standardized, community-based version of the MET.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".