International nursing leadership related to the drugs phenomenon: a case study of the partnership experience between the Inter-American Drug Abuse Control Commission (CICAD) and the University of Alberta - Faculty of Nursing
Bibliographic record
Abstract
In this article, the authors discuss the value of international health in advancing the nursing profession through the development of strong leadership in the area of drug demand reduction. Paradigms for nursing leadership are briefly reviewed and linked to the development of the "International Nursing Leadership Institutes" organized by the Inter-American Commission for the Control of Drug Abuse (CICAD). The "International Nursing Leadership Institutes" have facilitated the implementation of Phase III of the CICAD Schools of Nursing Project: a) planning and implementing the first "International Research Capacity-Building Program for Nurses to Study the Drug Phenomenon in Latin America", b) development of Regional and National Strategic Plans for Nursing Professionals in the Area of Demand Reduction in Latin America, and c) preparation of a document that provides guidelines on how to include drug content into undergraduate and graduate nursing curricula. The article also brings reflections directly from several of the participants in the first International Research Capacity-Building Program for Nurses to Study the Drug Phenomenon in the Americas, offered in collaboration with the Faculty of Nursing at the University of Alberta in Edmonton, Canada. These reflections demonstrate the multiplicity of ways in which this capacity-building program has made it easier for these members of Latin American Schools of Nursing to show leadership in the area of drug demand reduction.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.016 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".