Preliminary inter-rater reliability of the wheelchair components questionnaire for condition
Bibliographic record
Abstract
PURPOSE: The Wheelchair Components Questionnaire for Condition (WCQ-C) enables the collection of data on wheelchair maintenance condition and durability in resource-limited environments. It can be used in large studies to indicate typical patterns of wear at a location, or for a type of wheelchair. It can also be used in clinical settings as an evidence based indication that a wheelchair may need repair or replacement. This type of data can enable effective use of limited funds by wheelchair providers, manufacturers and users. The goal of this study was to investigate the inter-rater reliability of the WCQ-C. METHODS: Two therapists from North America who have worked extensively in low-resource areas used the WCQ-C to independently evaluate 46 wheelchairs at a primary school for children with disabilities in Kenya. RESULTS: Mean scores of ratings for each wheelchair by the two raters were used to calculate a two-way random interclass correlation coefficient. A value of 0.82 with a 95% confidence interval of 0.67-0.89 indicated good preliminary reliability. CONCLUSION: Preliminary results indicate that the WCQ-C is a reliable method of assessment. Additional studies are needed with larger and more diverse groups of raters. Because WCQ-C findings are specific to wheelchair wear and maintenance at each location, studies at other locations are also needed. Implications for rehabilitation The importance of inter-rater reliability testing in confirming the reliability of an assessment tool such as the WCQ-C. The use of the WCQ-C to monitor wheelchair condition in low-resource settings and other field settings. If used at regular interval can produce data that can be used to describe typical changes over time at each individual setting. This could enable proactive planning at that setting to avoid typical breakdowns and the injuries or clinical complications that could result. The use of the WCQ-C to monitor the condition of groups of wheelchairs of the same type. It can describe typical patterns of wear and failure in a way that enables responsive change by manufacturers and designers. This enables more effective use of limited funds. On an individual basis, the use of the WCQ-C to alert users and health professionals of a need for repair or replacement. This could minimize the clinical problems and accidents that can result from wheelchair breakdown. Assessment of a wheelchair using the WCQ-C could provide evidence based data to insurance companies or wheelchair providers which indicates a need for wheelchair repair or replacement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.005 |
| 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".