The Best Practice Spotlight Organizations (BPSO) Program Experience in Australia
Bibliographic record
Abstract
"Introduction: The Registered Nurses' Association of Ontario's flagship Best Practice Spotlight Organization® Program is scaling up implementation around the globe. The Australian Nursing and Midwifery Federation has successfully partnered with the Registered Nurses' Association of Ontario's to become a Best Practice Spotlight Organization Host, and establish an evidence-based culture in South Australia. Australian Nursing and Midwifery Federation is the largest professional organisation and trade union for nurses, midwives and assistants in nursing in Australia with a commitment to high standards of professional practice, effective bargaining and industrial representation of members as well as a progressive, social justice based approach to policy issues facing the health system (Australian Nursing and Midwifery Federation 2017). Objective: Reflect on the experience of Australian Nursing and Midwifery Federation in implementing the Best Practice Spotlight Organization Program in nursing in South Australia. Methodology: To describe the process of scaling up the Registered Nurses' Association of Ontario's Best Practice Guidelines program in South Australia through its Best Practice Spotlight Organizations world renowned feature, barriers and opportunities to its uptake and its evaluation. Conclusions: Evaluation of the Best Practice Spotlight Organizations program demonstrates the value of practice reforms, which were implemented and improved outcomes for patients/clients, improved professional satisfaction and capacity, and created cost efficiencies in the pilot sites. Hurley J, Dabars E, Bonner R. The Best Practice Spotlight Organizations (BPSO) Program Experience in Australia."
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".