Reliability and responsiveness of the Self-Efficacy in Assessing, Training and Spotting wheelchair skills (SEATS) outcome measure
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the internal consistency, test-retest reliability and responsiveness of the Self-Efficacy in Assessing, Training and Spotting manual wheelchair skills (SEATS-M) and Self-Efficacy in Assessing, Training and Spotting power wheelchair skills (SEATS-P). METHODS: A 2-week test-retest design was used with a convenience sample of occupational and physical therapists who worked at a provincial rehabilitation centre (inpatient and outpatient services). Sixteen participants completed the SEATS-M and 18 participants completed the SEATS-P. RESULTS: ) ranged from 0.81 to 0.95, the standard error of measurements (SEM) ranged from 5.06 to 8.70 and the smallest real differences (SRD) ranged from 6.24 to 8.18. For the SEATS-P assessment, training, spotting and documentation sections, Cronbach's alpha coefficients ranged from 0.83 to 0.92, the ICCs ranged from 0.72 to 0.86, the SEMs ranged from 4.54 to 8.91 and the SRDs ranged from 5.90 to 8.27. CONCLUSIONS: There is preliminary evidence that both the SEATS-M and the SEATS-P have high internal consistency, good test-retest reliability and support for responsiveness. These tools can be used in evaluating clinician self-efficacy with assessing, training, spotting and documenting wheelchair skills included on the Wheelchair Skills Test. Implications for Rehabilitation There is preliminary evidence that the SEATS-M and SEATS-P are reliable and responsive outcome measures that can be used to evaluate the self-efficacy of clinicians to administer the Wheelchair Skills Program. Measurement of clinicians' self-efficacy in this area of practice may enable an enhanced understanding of the areas in which clinicians lack self-efficacy, thereby informing the development of improved knowledge translation interventions.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".