Co-development of a national, bilingual, post-licensure accredited educational program for registered nurses in primary care: A knowledge-to-action exemplar
Bibliographic record
Abstract
Registered nurses' practice in primary care varies and is sometimes sub-optimal. To fill the gap in primary care-specific knowledge, we co-constructed a national educational program to reinforce the nursing workforce. We based our project on the knowledge-to-action approach. Many lessons were learned during the development phase: (1) The experiential knowledge of patient partners and stakeholders allows an education program based on real needs; (2) The development of a national education program requires high-intensity investment from all involved persons; (3) An in-person meeting at the beginning of the project enables robust discussions and optimal co-creation; and (4) In a country where two official languages are spoken, it's essential to create a safe environment and a translation infrastructure that allows everyone to express themselves in the language of their choice. Finally, other initiatives in healthcare education or professional practice improvement could leverage our findings to realize national-scale projects using knowledge creation approaches.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".