Perceptions of health professionals about effective practice in falls prevention
Bibliographic record
Abstract
PURPOSE: Falls prevention is a practice context with a rapidly expanding evidence-base. However, little is known about the implementation of this evidence into practice by health professionals. This study aimed to explore how falls prevention evidence is applied in practice internationally by health professionals working in the homes of older people, and to identify the perceived barriers and effective strategies in implementing falls prevention programs. METHOD: A qualitative study design using a grounded theory approach was selected. Data were collected via focus groups or individual, semi-structured interviews with 50 health professionals from Australia, the UK and Canada. All participants visited older people in their homes as part of their usual practice. Data analysis used the constant comparative method. RESULTS: Three themes emerged from the data: (i) client experiences of falls prevention, (ii) professional skills and clinical reasoning in falls prevention and (iii) service issues in falls prevention. CONCLUSIONS: The complexity of delivering an evidence-based, multi-disciplinary falls prevention intervention that is acceptable to clients was described by participants. Challenges were identified in applying the evidence according to the resources and experience of health professionals in the systems within which they work.
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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 | 0.020 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".