Creation of an evidence-based practice reference model in falls prevention: findings from occupational therapy
Bibliographic record
Abstract
Purpose: This study attempted to capture the evidence-based practice (EBP) behaviours of expert occupational therapy (OT) clinicians in order to develop a reference model of EBP in falls prevention. Methods: Expert clinicians participated in the creation of a clinical vignette through focus group discussions. Using the vignette as the stimulus case, the same clinicians answered questions that reflected the EBP process. Validation of original responses and data synthesis occurred through a second focus group. This validation process resulted in the elaboration of a tree structure EBP decision model. Results: Findings show that clinicians are not expert evidence-based practitioners. Although some of the experts’ clinical decisions were based on a combination of professional experience and research evidence, clinicians relied primarily on clinical experience for more complex aspects of decision-making. When explicitly instructed to answer questions corresponding to the five EBP steps, experts were compelled to think about the use of evidence and could proceed through the EBP process. Conclusions: The model represents the expert clinical decisions in each of the EBP steps and illustrates what aspects of the decision-making process are in line with EBP versus aspects that are driven primarily by experience. This research has the potential to assist clinicians working in prevention of falls in geriatric rehabilitation who can use the model as a practice framework to guide them through the EBP process.Implications for RehabilitationExperienced clinicians can proceed through the steps of the evidence-based practice process with guidance and scaffolding.Clinical experience is a major factor in decision-making in falls prevention.Models of expert performance provide useful insights into decision-making outcomes and evidence-based practise behaviours of experienced clinicians in falls prevention.
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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 | 0.089 | 0.261 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".