EVALUATION OF A CANADIAN PRIMARY CARE FALL PREVENTION TRAINING AND RESOURCE PACKAGE
Bibliographic record
Abstract
Background Few physicians have the training/resources to include evidence-based fall prevention (FP) strategies as part of their standard practice. Aims/Objectives/Purpose To address this, a Primary Care Fall Prevention (PCFP) package was developed including: an instructional video; fact sheets; a fall risk checklist; and patient handouts. Materials were evaluated to determine effectiveness of the resources to increase knowledge and/or bring about change in physician practice. Methods A pre/post survey on fall-related knowledge, use of FP resources/strategies was conducted with family physicians recruited from those with substantial numbers of elderly patients. After applying the package over 4–6 weeks, 11 participated in in-depth phone interviews; 10 provided a follow-up survey. Results/Outcomes Out of a max of 17, the average ‘fall-related knowledge’ pre score=10.2 (SD=2.5) and post-survey=13.2 (SD=1.3) (t (9)=2.7, p<0.03). After reviewing resources, 60% were more likely to screen for fall risk; 30% more likely to assess mobility and/or balance; 50% more likely to give fall risk handouts; and 20% more likely to provide information on medication/fall risk. After reviewing materials on fall-prevention strategies, 30% were more likely to provide education on home safety/physical activity/balance exercises; 50% more likely to refer to PT/OT; 40% more likely to provide Vit D guidelines, 50% more likely to provide calcium intake guidelines; and 20% more likely to make referrals to FP programmes. Interviews revealed insights into barriers and facilitators to PCFP. Significance/Contributions to the Field The PCFP package increased knowledge and showed some improvements in FP practice among physicians.
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".