Fall Video Analysis Questionnaire (FVAQ) Short Form Version (appears in: Development and Validation of a Questionnaire for Analyzing Real-Life Falls in Long-Term Care Captured on Video). Copyright: Creative Commons License.
Bibliographic record
Abstract
Survey is in the additional file in the Viewing Options box. ABSTRACT: BACKGROUND: Falls are the number one cause of injuries in older adults, and are particularly common in long-term care (LTC). Lack of objective evidence on the mechanisms of falls in this setting is a major barrier to prevention. Video capture of real-life falls can help to address this barrier, if valid tools are available for data analysis. To address this need, we developed a 24-item fall video analysis questionnaire (FVAQ) to probe key biomechanical, behavioural, situational, and environmental aspects of the initiation, descent, and impact stages of falls. We then tested the reliability of this tool using video footage of falls collected in LTC. METHODS: Over three years, we video-captured 221 falls experienced by 130 individuals in common areas (e.g., dining rooms, hallways, and lounges) of two LTC facilities. The FVAQ was developed through literature review and an iterative process to ensure our responses captured the most common behaviours observed in preliminary review of fall videos. Inter-rater reliability was assessed by comparing responses from two teams, each having three members, who reviewed 15 randomly-selected videos. Intra-rater reliability was measured by comparing responses from one team at baseline and 12 months later. RESULTS: In 17 of the 24 questions, the percentage of inter- and intra-rater agreement was over 80% and the Cohen's Kappa was greater than 0.60, reflecting good reliability. These included questions on the cause of imbalance, activity at the time of the fall, fall direction, stepping responses, and impact to specific body sites. Poorer agreement was observed for footwear, contribution of clutter, reach-to-grasp responses, and perceived site of injury risk. CONCLUSIONS: Our results provide strong evidence of the reliability of the FVAQ for classifying biomechanical, behavioural, situational, and environmental aspects of falls captured on video in common areas in LTC. Application of this tool should reveal new and important strategies for the prevention and treatment of falls and fall-related injuries in this setting.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".