Novel Application of the World Health Organization Community-Based Rehabilitation Matrix to Understand Services’ Contributions to Community Participation for Persons With Traumatic Spinal Cord Injury
Bibliographic record
Abstract
PURPOSE: The aim of the study is to use the World Health Organization community-based rehabilitation matrix for understanding services' contributions to foster community participation for people with traumatic spinal cord injury. METHODS: This study used a convergent mixed-methods design with a quantitative arm describing the frequency with which services contributed to 22 of the community-based rehabilitation-matrix elements and a qualitative arm involving document reviews and stakeholder interviews. Results were integrated following Onwuegbuzie and Teddlie's method (i.e., quan + QUAL). RESULTS: Twenty of the 22 (91%) of the World Health Organization community-based rehabilitation elements were addressed by traumatic spinal cord injury services. Five types of services were identified. Integrated results showed that the strengths of traumatic spinal cord injury services were as follows: (1) comprehensiveness; (2) essential medical services publicly funded; (3) numerous social protections available; and (4) highly active community-based organizations. Identified opportunities to improve these services were as follows: (1) increase specificity for traumatic spinal cord injury and (2) increase communication and integration among services. CONCLUSIONS: Services available for people with traumatic spinal cord injury in the province studied address most of the elements of the World Health Organization community-based rehabilitation matrix. However, lack of cohesion between services could create gaps that hinder community participation. Addressing these gaps could improve the quality of life and outcomes of people with traumatic spinal cord injury.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".