Is neocolonialism existing in global surgery practice? An analysis of a web-based survey amongst global surgery practitioners
Bibliographic record
Abstract
Background There is an ongoing discussion for decolonization of global health and a resetting of global health partnerships and practices. However, a lack of understanding and agreement on the issues involved remain a major limitation. The aim of this study was to understand and identify the manifestations of neocolonialism in global surgery practice. Methods This was a qualitative web-based survey of 445 low-and middle-income countries (LMICs) and high-income countries (HICs) global surgery practitioners. We also captured through focussed interviews their perceptions and reported manifestations of neocolonialism in global surgery. Results The majority (73.9%) came from LMICs, while 26.1% were from HICs. Surgeons formed the largest group (57.6%), with many having extensive experience (38.7% with over 10 years in global surgery). Neocolonialism was defined as an unequal power dynamic favoring HIC agendas. Uncompensated work by LMICs staff and funding disparities were identified as neocolonial practices by HICs participants. Limited research capacity and frustrated LMICs providers were seen as consequences. Factors enabling neocolonialism included limited local funding and training priorities set by funders, not local needs. More than 75% of participants agreed that fear of losing HIC support was a major barrier to open communication about neocolonialism in global surgery. Conclusions This study among global surgery players unbderscores existence and experiences of neocolonialism in global surgery. The impact of this practice and the enablers need to be urgently addressed by implementing mitigating solutions.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".