An instrument to measure mobile shower commode usability: the eMAST 1.0
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a preliminary psychometric evaluation of the electronic mobile shower commode assessment tool (eMAST) 1.0. Design/methodology/approach A cross-sectional validation study was undertaken with 32 adults with spinal cord injury (SCI), aged 18 years or older, who use mobile shower commodes for toileting and/or showering. The eMAST 1.0, Quebec user evaluation of satisfaction with assistive technology, Version 2.0 (QUEST 2.0), and modified system usability scale (SUS) were administered online via SurveyMonkey. The eMAST 1.0 was re-administered approximately seven days later. Psychometric properties of internal consistency, test-retest reliability, and convergent validity were assessed. Findings As hypothesised, the eMAST 1.0 demonstrated strong internal consistency (Cronbach’s α=0.73, N=32); acceptable test-retest reliability (intra-class coefficient (3, 1)=0.75 (0.53-0.88, 95 per cent confidence interval) (n=27)); and strong, positive correlations with the QUEST 2.0’s devices subscale and modified SUS (Pearson’s correlation coefficients 0.70 and 0.63, respectively). Research limitations/implications The sample was not fully representative of Australian data in terms of gender, or state of residence, but was representative in terms of SCI level. Age data were not assessed. The sample size was small but adequate for a preliminary psychometric evaluation. Originality/value The preliminary psychometric evaluation indicates the eMAST 1.0 is a valid and reliable instrument that measures usability of MSCs for adults with SCI. It may be useful for exploring relationships between usability and satisfaction of MSCs.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".