Clinical Observership Opportunities in North America for International Orthopaedic Surgeons
Bibliographic record
Abstract
BACKGROUND: Although efforts have been made to address the inequities of surgical care globally, to our knowledge, there has been no comprehensive analysis of orthopaedic clinical observerships in North America that are available for international surgeons. METHODS: Two investigators performed a systematic online search to identify orthopaedic clinical observerships that are available in the United States and Canada for international surgeons. Variables such as host type, geographic location of host site, program type, eligibility criteria, subspecialty focus, application and participation fees, availability of funding, duration of observership, and the quality of online information that is available based on an online content (OC) score were collected. RESULTS: Of the 113 available observership sponsors in North America, 36 (32%) were professional-society-based, 69 (61%) were academic/institution-based, and 8 (7%) were private-practice-based. Most observerships were located in the U.S. (n = 85) and, of these, the Northeast was the most common U.S. region (n = 29, p = 0.008). Of the observerships with a focus, pediatrics was the most frequent orthopaedic subspecialty (p < 0.0001), followed by spine and trauma. Professional-society-sponsored observerships offered funding to international surgeons more often than academic/institution-based and privately sponsored programs (p < 0.0001). The average OC score for the entire cohort was 2.35 and was similar among the 3 host types (p = 0.954). The program structure and requirements such as applicant eligibility, application and participation fees, and duration of observership varied widely. CONCLUSIONS: There are opportunities for international orthopaedic surgeons to participate in clinical observerships in North America. Given the greater funding support and lack of fees for professional-society-sponsored observerships, these observerships may pose fewer financial barriers for surgeons from low and middle-income countries (LMICs). The quality of online information was similar among the 3 different host types and can be improved. CLINICAL RELEVANCE: The relevance and impact of a clinical observership experience in North America for a practicing orthopaedic surgeon from an LMIC need to be explored further.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".