Surgical volunteerism or voluntourism – Are we doing more harm than good?
Bibliographic record
Abstract
The significant rise in the number of international health electives undertaken by medical students and doctors in the US, Canada and UK reflects acknowledgement of the inter-connected nature of these challenges to health systems and the drive to help solve them. However, the next generation of international volunteers often operate under a conflicting duality: whilst many of their role models have devoted their lives to global health following a similar volunteering experience, there are pervasive ethical problems associated with transient global health work that must be identified and addressed to ensure positive outcomes for all parties involved. The majority of populations served by shortterm surgical volunteer trips are vulnerable communities; this raises ethical questions such as the lack of informed consent, use of unauthorised photos for marketing, and practicing new procedural techniques. 2 Whilst there exist various models that can be used to facilitate effective international health electives, there is a lack of stringent monitoring and enforcement both on the part of healthcare institutions deploying volunteers as well as recipient bodies in LMICS. Well-organised programmes prevent cases of 'poor care given to poor people' as medical students and doctors are expected to act in their patients' best interests as they would do in their home country. As clinician interest in global health projects continue to rise, too-common trainee naivety - while rooted in goodwill - must be supplanted by adequate training, ethical coherence, and cultural fluency. The onus lies on medical schools and healthcare bodies endorsing international electives to ensure that individuals are appropriately prepared and only travel through programmes that are able to demonstrate that they meet the necessary requirements and follow guidelines to avoid doing more harm than good.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".