Partnering to build surgical capacity in low-resource settings: a qualitative study of Canadian global surgeons
Bibliographic record
Abstract
OBJECTIVES: This qualitative study aimed to explore the perspectives of Canadian global surgeons with experience developing surgical education partnerships with low- and middle-income countries (LMICs) for the purpose of identifying factors for success. DESIGN: A purposive sample of leaders from global surgery programmes at Canadian Faculties of Medicine participated in virtual semi-structured interviews. A six-phase thematic analysis was performed using a constructivist lens on verbatim transcripts by three independent researchers. Key factors for success were thematically collated with constant comparison and inter-investigator triangulation in NVivo software until theoretical saturation was reached. PARTICIPANTS: Fifteen surgeons, representing 11 subspecialties at 6 Canadian academic institutions and a combined experience across 6 continents, were interviewed between January and June 2022. RESULTS: Four facilitators for success of global surgery training programmes were identified, with a strong undertone of relationship-building permeating all subthemes: (1) facilitative skill sets and infrastructure, (2) longitudinal engagement, (3) local ownership and (4) interpersonal humility. Participants defined facilitative skill sets to include demonstrated surgical competence and facilitative infrastructure to include pre-existing local networks, language congruency, sustainable funding and support from external organisations. They perceived longitudinal engagement as spanning multiple trips, enabled by strong personal motivation and arrangements at their home institutions. Ownership of projects by local champions, including in research output, was noted as key to preventing brain drain and catalysing a ripple effect of surgical trainees. Finally, interviewees emphasised interpersonal humility as being crucial to decolonising the institution of global surgery with cultural competence, reflexivity and sustainability. CONCLUSIONS: The interviewed surgeons perceived strong cross-cultural relationships as fundamental to all other dimensions of success when working in low-resource capacity-building. While this study presents a comprehensive Canadian perspective informed by high-profile leadership in global surgery, a parallel study highlighting LMIC-partners' perspectives will be critical to a more complete understanding of programme success.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.025 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| 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".