Further Development of the Big Store Multiple Errands Test
Bibliographic record
Abstract
The Multiple Errands Test (MET) is a tool for assessing the effects of executive function impairments, common among those with acquired brain injury, on everyday life function. The objective of this study was to further establish inter-rater reliability and explore known group validity of the Big-Store MET in distinguishing between adults with an ABI and matched healthy controls, and to explore the effects of environmental factors on Big-Store MET performance. Participants (n = 5 community-dwelling people with ABI, n = 4 healthy controls) were administered the Big Store MET by one of two trained raters. Inter-rater reliability was examined using intra-class correlation coefficients. Known-group validity was examined using Cohen’s d effect size, and the effects of environmental load and familiarity on performance were examined descriptively. The results showed the inter-rater reliability was very high for all MET performance scores (ICC =0.74–1.00). Effect sizes for known group validity were moderate to large (d = 0.48–1.06) on five of six performance scores on the MET. Descriptively, control participants performance was better with higher store familiarity and lower environmental load whereas the opposite was found for participants with ABI. This research suggests the Big-Store MET may be a clinically useful tool and highlights the importance of further 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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".