Characteristics and Consequences of Falls in People Who Use Wheelchairs in Long-Term Care Settings
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study was to understand the characteristics and consequences of falls in individuals using wheelchairs in long-term care settings. DESIGN: Observational analysis of real-world falls in long-term care. SETTING AND PARTICIPANTS: Residents using wheelchairs from 2 long-term care facilities in British Columbia, Canada (n = 32 participants, mean age = 84.7 years, 12 women). METHODS: Two raters used the validated Falls Video Analysis Questionnaire, adapted from the original version, to assess the causal, behavioral, and environmental aspects of falls from wheelchairs. RESULTS: A total of 58 wheelchair fall videos were identified out of 300 total videos that were collected from 2007 to 2014. Wheelchair falls were most often caused by incorrect transfer or shift of body weight (70.7%). Participants most often fell backward with 89.7% striking their pelvis. Individuals using wheelchairs had limited protective response, with only 10.3% demonstrating a step response. Improper brake position contributed to 67.2% of falls. No serious fall-related injuries were reported. CONCLUSIONS AND IMPLICATIONS: The findings highlight the unique nature of falls in older adults who use wheelchairs in long-term care settings. Overall, the results of this study support clinical practice and the critical need for developing specialized fall prevention and fall detection interventions for individuals who use wheelchairs in long-term care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".