An international internship on social development led by Canadian nursing students: Empowering learning
Bibliographic record
Abstract
Background A Canadian nursing student-led knowledge dissemination project on health promotion for social development was implemented with local professionals and communities in Brazil. Objectives (a) to identify how student-interns contrasted Canadian and Brazilian cultural and social realities within a primary healthcare context from a social development perspective; (b) to examine how philosophical underpinnings, including social critical theory and notions of social justice, guided student-interns in acknowledging inequalities in primary healthcare in Brazil; and (c) to participate in the debate on the contribution of Canadian nursing students to the global movement for social development. Design and Setting A qualitative appraisal of short-term outcomes of an international internship in the cities of Birigui & Araçatuba (São Paulo-Brazil). Participants Four Canadian fourth-year undergraduate nursing students enrolled in a metropolitan university program. Methods Recruitment was through an email invitation to the student-interns, who accepted, and signed informed consent forms. Their participation was unpaid and voluntary. One-time individual interviews were conducted at the end of their internships. Transcriptions of the audio-recorded interviews were coded using the qualitative software program ATLAS ti 6.0. The findings were analyzed using thematic analysis. Results Student-interns' learning unfolded from making associations among concepts, new ideas, and their previous experiences, leading to a personal transformation through which they established new conceptual and personal connections. The two main themes revealed by the thematic analysis were dichotomizing realities, that is, acknowledging the existence of “two sides of each situation,” and discovering an unexpected reciprocity between global and urban health. Furthermore, the student-interns achieved personal and professional empowerment. Conclusions The knowledge gained from the international experience helped the student-interns learn how to collaborate with Brazilian society's sectors to improve the social conditions of a “marginalized population”. Student-interns became aware of their inner power to promote change by making invisible inequity visible in their own terms.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".