Impact of wheelchair acquisition on social participation
Bibliographic record
Abstract
PURPOSE: Efficient mobility could be a prerequisite to carrying out many daily activities and social roles (social participation). The aim of this study was to assess the impact of wheelchair acquisition on social participation. METHODS: Single group pre/post design where the intervention was the acquisition of a wheelchair paid for by the provincial government. Data were collected retrospectively from the participants' medical files. Individuals were excluded if they received an assistive device other than a wheelchair or contacted the centre only for wheelchair repairs. Social participation was measured using the Reintegration to Normal Living Index (RNLI) questionnaire. RESULTS: The sample (n = 42) had a mean age of 64.2 +/- 18.5 years, and 50% of them (n = 21) did not have a wheelchair before the intervention. The total RNLI scores pre- (46.9/100 +/- 24.7) and post-acquisition (29.7/100 +/- 18.5) showed a significant improvement in participation (p < 0.001). No difference was found between those who had their first wheelchair (n = 21) compared with replacement. Single-item analysis of the RNLI showed a significant difference for 5 of the 11 items. Age and diagnosis were significantly correlated (p < 0.05) with some of the items. CONCLUSION: Social participation improved significantly following wheelchair acquisition although confounding variables may have contributed to this improvement.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".