Fall prevention indicator priorities for public health and across health sectors in Ontario: a comparative study
Bibliographic record
Abstract
INTRODUCTION: Falls are the leading cause of injury-related emergency department (ED) visits and hospital admissions among older adults across many provinces in Canada. To effectively address this burden requires relevant data and indicators to inform fall prevention planning and evaluation for practitioners across the spectrum of prevention. METHODS: We used a modified Delphi approach, including an environmental scan, survey and pairwise comparison exercise to identify, refine and prioritize older adult fall prevention indicators across multiple health sectors in Ontario and specifically for public health. Three iterative phases of consultation were conducted with practitioners, as well as experts in injury prevention indicator development. RESULTS: The prioritization exercise resulted in differing priorities between multiple sectors and public health. The highest ranked indicator for multiple sectors was the rate of ED visits, and the lowest was disability-adjusted life years due to a fall. For public health, the rate of hospitalizations due to a fall was ranked first, with the rate of mortality due to a fall last. The remainder of the list differs considerably by group, with certain indicators ranked on one list, but not the other. CONCLUSION: This work identified, refined and prioritized indicators for older adult fall prevention across health sectors and for public health in Ontario. While both groups shared some highly ranked indicators, their differing responsibilities in fall prevention are reflected in the contents and order of their respective priorities for indicators. Delineating the unique data needs of each group highlights the importance of having consistent and actionable data that informs prevention planning and evaluation.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| 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".