Facilitating Guideline Implementation in Primary Health Care Practices
Bibliographic record
Abstract
Introduction: Many patients continue to receive suboptimal services, inappropriate, unsafe, and costly care. Underutilization of research by health professionals is a common problem in the primary care setting. Although many theoretical frameworks can be used to help address such evidence-practice gaps, health care professionals may not be aware of the benefits of frameworks or of the most appropriate ones for their context and thus, may be faced with the challenge of selecting and using the most relevant one. Aim: The aim of this article was to describe the process used to adapt a knowledge translation framework to meet the local needs of health professionals working in one large primary care setting. Methods: The authors developed a 5-step approach for guideline implementation. This approach was informed by prior research and the authors’ experiences in supporting multidisciplinary teams of health care professionals during the implementation of evidence-based clinical guidelines into primary care practices. To ensure that the 5-step approach was practical and suitable for the context of guideline implementation by multidisciplinary teams in primary health care, the implementation team adapted the “knowledge-to-action” framework using a multistep process. Results: The implementation approach consisted of the following 5 steps: identification, context analysis, development of implementation plan, evaluation, and sustainability. All 5 steps were described alongside details about a national low back pain project. Discussion: This article describes a collaborative, grassroots process that addressed an identified need in one complex context by adapting a knowledge translation framework to meet the local needs of health professionals working in primary care settings. Existing implementation frameworks may be too complex or abstract for use in busy clinical contexts. The 5-step approach presented in this paper resulted in practical steps that are more readily understood by health care professionals and staff on “the ground.”
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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 | 0.146 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".