International research capacity-building programs for nurses to study the drug phenomenon in Latin America: challenges and perspectives
Bibliographic record
Abstract
The First International Research Capacity-Building Program for Nurses to Study the Drug Phenomenon in the Americas is a result of a partnership between the Inter-American Drug Abuse Control Commission (CICAD) of the Organization of American States (OAS) and the Faculty of Nursing in the University of Alberta, with financial support from the Government of Canada. The program was divided into two parts. The first part of the program was held at the University of Alberta in Edmonton, Alberta, Canada. It involved capacity-building in research methodologies at the Faculty of Nursing, which lead to the preparation of four multi-centric research proposals for drug demand reduction in the home countries of the eleven participants in the program. The second part of the program was related to the implementation of multi-centric research proposals in seven countries in Latin America and in Canada. This program presented expertise in research methodology to members of Latin American Schools of Nursing and introduced Latin American expertise to members of a Canadian Faculty of Nursing. The International Research Capacity-Building Program for Nurses to Study the Drug Phenomenon in the Americas has fostered the kind of inter-cultural respect and mutual appreciation necessary to confront the global health problem of the abuse of both licit and illicit drugs.
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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 | 0.087 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.010 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 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".