Bibliographic record
Abstract
Abstract This project brought together a team of researchers and decision-makers to conduct policy-relevant research to support the introduction of advanced nursing practice roles in British Columbia. All team members, including decision-makers, were actively involved in the conceptualization, design, data collection, analysis and interpretation of the study. This level of engagement, coupled with ongoing knowledge translation (KT) activities, led to the implementation by stakeholders of a majority of the study’s recommendations. The results have since been used to guide legislative and regulatory development and to design a nurse practitioner education program. Resume Ce projet regroupait une equipe de chercheurs et de decideurs qui se sont reunis pour effectuer de la recherche liee aux politiques en vue d’appuyer l’introduction de roles avances dans la pratique des soins infirmiers en Colombie-Britannique. Tous les mem-bres de l’equipe, y compris les decideurs, ont pris une part active a la conceptualisa-tion, a la conception, a la collecte de donnees, a l’analyse et a l’interpretation de l’etude. Grâce a ce niveau d’engagement et a des activites continues d’application des connais-sances (AC), la majorite des recommandations de l’etude ont ete mises en œuvre par les intervenants. Les resultats ont depuis ete utilises pour orienter l’elaboration de mesures legislatives et reglementaires et pour concevoir un programme d’enseignement a l’intention des infirmieres praticiennes.T
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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 | 0.114 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".