Evaluating a Paramedic-Led Fall-Referral Program in Nova Scotia: a Mixed-Methods Study
Bibliographic record
Abstract
Background Falls in older adults are a worldwide health issue, and lead to high morbidity, mortality, and healthcare costs. Paramedics play a unique and important role in post-fall management. The objectives of this study were to measure the frequency with which paramedics made referrals to fall-prevention programs, understand the factors influencing these decisions, and compare outcomes between those who received a referral with those who did not. Methods This mixed-methods study evaluated a paramedic fall-referral program in Nova Scotia for older adults with non-transport dispositions after a 911 response. Patient demographics and outcomes were analyzd using a matched cohort approach, while paramedic beliefs regarding the program were explored using The Theory of Planned Behavior. Results From 2014 to 2019, a total of 289 referrals were made, and a matched cohort analysis (1:2) found no significant difference in the mean number of fall-related 911 calls in the following 12 months between those who were referred (m=0.31, SD=0.94) and those who were not (m=0.30, SD=1.28). Paramedics acknowledged the importance of fall prevention, but felt a lack of education, loop closure-feedback to the referring paramedic, and patient reluctance to consider the program, were all significant barriers to referral. Discussion This study assessed Nova Scotia’s paramedic fall-prevention referral program, revealing low referral frequency despite high numbers of fall-related 911 calls, and no significant reduction in relapse 911 calls. Barriers to referral included patient reluctance, poor systematization, and lack of education and feedback. Conclusion The study highlights opportunities for improving referral systems, as paramedics play a bigger role in the prevention of age-related health issues such as falls.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".