Defining a Fall and Reasons for Falling: Comparisons Among the Views of Seniors, Health Care Providers, and the Research Literature
Bibliographic record
Abstract
PURPOSE: The purpose of this study was (a) to obtain information about the perceptions held by seniors and health care providers concerning what constitutes a fall and potential reasons for falling, and (b) to compare these perceptions to the research literature. DESIGN AND METHODS: As part of a larger telephone survey, interviewers asked 477 community-dwelling seniors to define a fall and to provide reasons for falling. In addition, we interviewed 31 health care providers from the community on the same topics. In order to capture patterns in conceptualized thinking, we used content analysis to develop codes and categories for a fall definition and reasons for falling. We reviewed selected articles in order to obtain a comprehensive overview of fall definitions currently used in the research and prevention literature. RESULTS: A fall had different meanings for different groups. Seniors and health care providers focused mainly on antecedents and consequences of falling, whereas researchers described the fall event itself. There were substantial differences between the reasons for falling as reported by seniors and the risk factors as identified in the research literature. IMPLICATIONS: If not provided with an appropriate definition, seniors can interpret the meaning of a fall in many different ways. This has the potential to reduce the validity in studies comparing fallers to nonfallers. Research reports and prevention programs should always provide an operational definition of a fall. In communication between health care providers and seniors, an appropriate definition increases the possibility for early detection of seniors in greater need of care and services.
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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 | 0.032 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".