Adaptation of a Best Practice Guideline to Strengthen Client‐Centered Care in Public Health
Bibliographic record
Abstract
Best practice guidelines (BPGs) were developed by the Registered Nurses Association of Ontario (RNAO) to support evidence-based nursing practice. One Ontario public health unit chose to implement the BPG on client-centered care (CCC). A critical review of this BPG revealed issues that would hinder successful implementation within a public health setting. These included a focus on the client as an individual, the predominance of acute care exemplars and training resources that were not representative of public health nursing practice, and the need to reconcile the enforcement roles of public health with the BPG principles. The purpose of this article is to describe the process of adapting the CCC BPG to more accurately reflect the broad scope of public health nursing practice. A model for CCC in public health nursing context is presented and processes for implementing, evaluating, and sustaining CCC are described.
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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 | 0.174 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".