Understanding contextual factors in falls in long-term care facilities
Bibliographic record
Abstract
Purpose – Despite the growing area of research involving falls in the residential care setting, the link between contextual and environmental factors in falls is poorly understood. This paper aims to draw upon existing research being undertaken in long-term care (LTC) in Metro Vancouver, Canada, with a particular focus on identifying contextual factors contributing to fall events. Design/methodology/approach – This paper presents the results of a qualitative observational analysis of video-captured data collected through a network of high-quality video systems in two LTC facilities. The research comprised workshops involving experienced researchers who reviewed six video sequences of fall events. The outcome of the workshops was a written narrative summarizing the discussion and researchers’ interpretation of fall sequences. Findings – The analysis indicates that there are a broad range of environmental, behavioral and situational factors that contribute to falls in LTC. This suggests that a limited conceptualization of a fall as an outcome of the person's impairment and environmental hazards fails to convey the complexity of potential contributory factors typical of most fall incidents. Research limitations/implications – Broadening our understanding of falls provides the potential to make recommendations for falls prevention practice across multiple levels, including the individual, social and organizational context. Originality/value – The paper evaluates the potential of video-based data in fall analysis and points to the development of a case study approach to analyzing fall incidents to capture the complex nature of contributory factors beyond research that focuses solely on intrinsic and extrinsic risk factors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".