A Qualitative Exploration of Proactive Falls Prevention by Canadian Primary Care Providers
Bibliographic record
Abstract
Background: Falls are a growing concern in Canada. Primary care providers are well positioned to address falls risk, but international literature suggests that best-practice guidelines are rarely followed. The objective of this study is to explore the perspectives of Canadian primary care providers around falls prevention and identify solutions. Methods: We conducted one-on-one qualitative interviews with a maximum variation sample of nine primary care providers in Ontario (n=8) and Alberta (n=1) in Canada. Data were collected over telephone and in-person at the location of participants choosing. Audio recordings of the interviews were transcribed, then coded and analyzed with the Behaviour Change Wheel theoretical framework. Results: Most participants reported relying on patient self-report, intuition, and reactive approaches to identifying falls risk. Reported barriers to falls prevention included low capability to gather information on patient history, context, and community resources; limited opportunity to manage patient complexity due to time constraints; and challenges with motivating patients to engage in care plans. Reported facilitators included team-based interprofessional care and provider motivation. Conclusions: This study has found that Canadian primary care providers face barriers to identifying and managing falls risk. These barriers may be rooted in primary care culture, structure, and tradition.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".