Facilitating Guideline Implementation in Primary Health Care Practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it