Using video-elicitation to assess risks and potential falls reduction strategies in long term care
Bibliographic record
Abstract
PURPOSE: To assess the ability to use and the usefulness of video-elicitation to study risks and potential ways to reduce transfer-related falls in long term care. METHOD: A qualitative research study was conducted in a long term care facility and included a purposeful sample of 16 subjects (6 residents, 6 health care providers, and 4 family members). Field observations, interviews, video-recordings of assisted transfers, and video-elicitation sessions were conducted with the participants. The interviews and video-elicitation sessions were digitally recorded, transcribed and coded independently by at least 2 analysts. The codes were organized under themes. RESULTS: Six themes related to risks and reduction of transfer-related falls were identified - environment, behaviors, health conditions, specific activities, knowledge and awareness, and balancing values. CONCLUSIONS: We were able to implement the novel participatory video-elicitation method developed and it was useful to identify risks and risk reduction strategies. Therefore, video-elicitation may be used in future studies to inform the design and testing of interventions to reduce transfer-related falls among LTC residents. Implications for Rehabilitation Falls are common among long term care residents. Visual-elicitation is a useful tool to be used in rehabilitation to assess risks and possible measures to reduce falls. The video-elicitation sessions optimized the ability and engaged residents, health care providers, and family members on providing information and discussing risks and potential measures to reduce transfer-related falls.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".