Factors Involved in Selection of a Career in Surgery and Orthopedics for Medical Students in Malawi
Bibliographic record
Abstract
AIMS: There is a critical shortage of Orthopedic Surgeons in Malawi as well as all countries in sub-Saharan Africa. To date, there is no published literature that has investigated surgical or Orthopedic career selection amongst African medical trainees. With the goal of facilitating recruitment into Surgery and Orthopedics in Malawi, we explored the key aspects of Malawian Medical Students' choice of careers in surgical disciplines. METHODS: An on-line survey of all students in clinical years at the College of Medicine in Blantyre, Malawi was performed. The survey was anonymous and constructed de novo by a stringent process including Item Generation, Item reduction, Survey composition, Pre-testing, Assessment of Validity by a recognized survey expert, Pilot testing in on-line format by several Malawian Medical Students, and then formal survey testing. RESULTS: Surgery was the most popular specialty choice among the medical trainees (46%). General Surgery was the popular surgical specialty (27%), followed by Neurosurgery (22%) and Orthopedics (19%). The majority of students (67%) feared occupational exposure to HIV but this did not appear to be a factor in specialty choice (p = 0.9). Students with Orthopedic mentors were significantly more likely to choose Orthopedics as their first choice surgical specialty (p = 0.01). Despite limited resources and surgeons in sub-Saharan Africa, surgical specialties are desirable career choices. CONCLUSIONS: This is the first evaluation of factors involved in surgical or Orthopedic career selection in any African context. Future initiatives to improve exposure and mentorship in Orthopedics are fundamental to recruitment into the specialty.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".