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International research capacity-building programs for nurses to study the drug phenomenon in Latin America: challenges and perspectives

2005· article· en· W2128153129 on OpenAlexafffundabout
Maria da Glória Miotto Wright, Catherine Caufield, Genevieve Gray, Joanne Olson

Bibliographic record

VenueRevista Latino-Americana de Enfermagem · 2005
Typearticle
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsUniversity of Alberta
FundersUniversity of AlbertaGovernment of Canada
KeywordsLatin AmericansGeneral partnershipGovernment (linguistics)Research programPhenomenonCapacity buildingPolitical scienceDemand reductionMedicineNursingPublic relations

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.782
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.143
GPT teacher head0.413
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2005
Admission routes3
Has abstractyes

Explore more

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