Utilization of the Seniors Falls Investigation Methodology to Identify System-Wide Causes of Falls in Community-Dwelling Seniors
Bibliographic record
Abstract
PURPOSE: As a highly heterogeneous group, seniors live in complex environments influenced by multiple physical and social structures that affect their safety. Until now, the major approach to falls research has been person centered. However, in industrial settings, the individuals involved in an accident are seen as the inheritors of system defects. The objective of the present study was to investigate safety deficiencies that contributed to falls in community-dwelling seniors using a systems approach. DESIGN AND METHODS: The investigations were conducted using the Seniors Falls Investigation Methodology (SFIM), an adapted version of a method used to examine transportation accidents, such as airplane crashes. Fifteen seniors, who experienced a fall or near fall, participated in multiple case studies. A cross-case synthesis was used to summarize findings and identify common patterns of causes and safety deficiencies. RESULTS: Falls and near falls are a result of latent unsafe conditions, and unsafe acts and decisions combined in a diverse set of circumstances. If not identified and removed, these unsafe conditions can cause falls for other seniors. IMPLICATIONS: This study provided compelling evidence that causes of falling are systemic and develop over time. It demonstrated that the systems approach is needed to expand the focus from the individual to multilayered organizational and supervisory causes. The SFIM demonstrated capability to identify causes of falls that will allow better prevention and management programs, hence advancing seniors' safety. SFIM shows great potential for implementation in organized settings, such as hospitals and long-term care homes.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".