Global Host Partner Perspectives: Utilizing a Conceptual Model to Strengthen Collaboration with Host Partners for International Nursing Student Placements
Bibliographic record
Abstract
OBJECTIVES: Collaboration in international nursing student placements requires equitable partnerships between global partners to address areas of shared importance, such as equity and justice in health promotion. This qualitative study was the first to use the Leffers and Mitchell Conceptual Model for Partnership and Sustainability in Global Health to elicit global host partners' perspectives regarding effective collaboration for Canadian community health nursing placements in the Dominican Republic. DESIGN AND SAMPLE: Focus group and semi-structured interview methodology was conducted with Dominican Republic (Dominican and Haitian) host partners (n = 23) about the engagement processes and host partner factors for effective partnership. RESULTS: Dominican (83%) and Haitian (17%) participants, comprised similar numbers of male and female adults aged 18-60 years (mean age = 36 years), represented the full range of the Dominican Republic host partners (e.g., teachers, health professionals). Interpretive analysis revealed themes that included (1) the unique role of the cultural broker; (2) relational collaboration in a collective society; (3) reciprocal approaches that honor local expertise; and (4) contextual socioeconomic and cultural factors that influence partnerships. CONCLUSIONS: Future research and implications at the individual, community, and policy levels are discussed.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".