Implementing Clinical Practice Guidelines in occupational therapy practice: Recommendations from the research evidence
Bibliographic record
Abstract
BACKGROUND: Clinical Practice Guidelines (CPGs) are prominent tools in evidence-based practice which integrate research evidence, clinical expertise and client input to develop recommendations for specific clinical circumstance. With the push to use research evidence in health care, it is anticipated that occupational therapists will become increasingly involved in implementing CPGs in practice. The research evidence has revealed several factors that can affect guideline uptake, and a variety of strategies that can facilitate implementation. METHODS: This narrative review examines the health-related literature in CPGs to answer the following questions. Based on the research evidence, (i) what are the factors that may influence guideline implementation? (ii) What implementation strategies may enhance guideline implementation? RESULTS: Factors within the guideline itself (e.g. quality, complexity and clarity), within the practitioner (e.g. experience, perceptions and beliefs), the patient (e.g. expectations and preferences) and the practice context (e.g. resource availability, organisational culture and opinion leaders) can all affect implementation success. Currently, there is no conclusive evidence to support the use of one implementation strategy over another, in all situations. The choice of implementation strategy must take into account the guideline to be implemented, the practice context and the anticipated challenges to implementation. CONCLUSIONS: By understanding the factors that can influence implementation and the strategies for successful implementation, occupational therapists will be better prepared to implement guidelines. Recommendations to assist with guideline uptake and implementation are provided.
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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 | 0.118 | 0.316 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".