Iran's Orthopaedic Landscape: Distribution, Per-capita Ratios, Female Inclusion, and Academic Standing among Residents and Surgeons.
Bibliographic record
Abstract
Objectives: Iran's orthopaedic surgery care is facing significant challenges due to an aging population and the increasing prevalence of chronic medical conditions such as osteoarthritis, fractures, and trauma. These challenges underscore the pressing need for a more equitable distribution of the orthopaedic workforce. This study aimed to assess the per capita ratios and geographical distribution of orthopaedic surgeons (OSs) in Iran, as well as their distribution in academic and non-academic settings. Additionally, the involvement and scientific productivity of women in orthopaedic s were examined. Methods: This study investigated the distribution, per-capita ratios, and academic status of OSs and trainees in Iran, and compared these parameters with those in Turkey and the UK. This study used data from the Islamic Republic of Iran Medical Council, the Iranian Scientometrics Information Database, and the population census to indicate an uneven distribution of OSs across Iran. Results: The per capita ratio of OSs in Iran (3.13) is lower than in Turkey (4.00) and the United Kingdom (8.00), highlighting disparities in healthcare infrastructure and economic resources in low-income countries. Notably, 33.6% of Iranian OSs reside in Tehran, contributing to unequal access to care. Furthermore, female representation in orthopaedic s remains limited, with only 3.5% of OSs being women. These academic surgeons have a median H-index of 4, which is lower than that of their counterparts in Canada and the United States. Conclusion: The study emphasized the significance of governmental reforms and incentives in promoting equitable distribution, gender diversity, and academic progress within Iran's orthopaedic workforce. Financial incentives, advanced facilities, and career advancement opportunities could enhance academic involvement and diversity. Improving the distribution of surgeons, increasing support for women in orthopaedic, and fostering academic interests are essential steps toward achieving equitable healthcare and boosting scientific output in Iran.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".