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Record W2553609625 · doi:10.15694/mep.2016.000124

The Toronto Addis Ababa Academic Collaboration in Nursing: Living the Concept of Counterparts

2016· article· en· W2553609625 on OpenAlexaboutno aff
Amy M. Bender, Fekadu Aga, Asrat Demissie, Amsale Cherie

Bibliographic record

VenueMedEdPublish · 2016
Typearticle
Languageen
FieldNursing
TopicNursing Education, Practice, and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsPaceReciprocity (cultural anthropology)CommissionValue (mathematics)NursingHealth careSociologyPublic relationsPolitical scienceMedicineSocial scienceGeography

Abstract

fetched live from OpenAlex

This article was migrated. The article was marked as recommended. A 2010 Lancet Commission on global higher education for the health professions called for institutional and instructional reforms worldwide in order to keep pace with pressing global health care needs. Among their many recommendations is an awareness of the value of international collaboration to enhance educational quality. In this article, we reflect on our experience of building such a collaboration in nursing. Using the concept of counterparts as articulated by DeSantis (1993; 1995), we describe the highlights as well as the lessons of our work together in developing the research component of the Masters of Science in Nursing (MScN) at Addis Ababa University in Ethiopia. We extend DeSantis' (1995) emphasis on roles in the relationship for understanding what constitutes success in international collaborations. Beyond identifying roles and responsibilities, our story calls attention to the value of cultivating relationships in which interdependence, mutuality, and issues of reciprocity are at the fore.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0190.025
Scholarly communication0.0140.008
Open science0.0010.012
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.019
GPT teacher head0.338
Teacher spread0.319 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations2
Published2016
Admission routes1
Has abstractyes

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