An Interdisciplinary Approach to Implementing a Best Practice Guideline in Public Health
Bibliographic record
Abstract
This article describes how one Ontario Public Health Unit implemented a best practice guideline throughout the organization and across disciplines to achieve best practice outcomes in the delivery of client-centered care. Integration of evidence-informed practice presents challenges for both implementation and sustainability. Applying a best practice guideline in the public health setting can add to the challenge. To address this, a variety of interventions were applied: building an interdisciplinary team, adapting a Registered Nurses' Association of Ontario Best Practice Guideline to reflect public health practice for nursing and other disciplines, developing a working definition of "client," engaging staff in knowledge translation, developing policy to support practice change, and incorporating client-centered care principles into daily practice. Outcomes indicate that nursing best practice guidelines, specific to client-centered care, can be successfully adapted and applied in public health practice. Considerations include the varied definitions of a "client," the various roles of public health professionals, and engagement of both internal and external clients. Moreover, interdisciplinary staff can apply the principles of client-centered care when working with clients and when engaging in education-, practice-, and policy-level initiatives to support evidence-informed practice.
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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 | 0.189 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.009 | 0.026 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".