Comparing the Oxford Digital Multiple Errands Test (OxMET) to a real-life version: Convergence, feasibility, and acceptability
Bibliographic record
Abstract
We aimed to assess the convergence, feasibility, and acceptability of the Oxford Digital Multiple Errands Test (OxMET) and the in-person Multiple Errands Test–Home version (MET-Home). Participants completed OxMET, MET-Home, Montreal Cognitive Assessment (MoCA), and questionnaires on activities of daily living, depression, technology usage, mobility, and disability. Forty-eight stroke survivors (mean age 69.61, 41.67% female, and average 16.5 months post-stroke) and 50 controls (mean age 71.46, 56.00% female) took part. No performance differences were found for healthy and stroke participants for MET-Home, and only found below p = .05 for OxMET but not below the corrected p = .006. Convergent validity was found between MET-Home and OxMET metrics (most r ≥ .30, p < .006). MET-Home accuracy was related to age (B = −.04, p = .03), sex (B = −.98, p = .03), disability (B = −0.63, p = .04), and MoCA (B = .26, p < .001), whereas OxMET accuracy was predicted by MoCA score (B = .40, p < .001). Feedback indicated that the OxMET was easy and fun and more acceptable than the MET-Home. The MET-Home was more stressful and interesting. The MET tasks demonstrated good convergent validity, with the OxMET digital administration providing a more feasible, inclusive, and acceptable assessment, especially to people with mobility restrictions and more severe stroke.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".