Explaining gender differences in transfer time to a trauma center in Northern Iran
Bibliographic record
Abstract
Background: The association between gender and time of receiving services (TRS) after traumatic injuries is rarely documented in developing countries. This study aimed to examine gender differences in time between occurring injuries and receiving services in hospital after trauma injuries in northern Iran. Materials and Methods: A total of 7085 injured patients were included in this study. Data on sociodemographic and clinical characteristics were extracted from the Guilan province trauma system registry (GTSR) from July 2017 to July 2018. The Oaxaca–Blinder (OB) method was used to explain the gender differences in the TRS after traumatic injuries. Results: There were significant differences between men and women in marital statues (P < 0.001), education level (P < 0.001), time of injury (P = 0.025), occupation (P < 0.001), type of trauma (P < 0.001), mode of transfer (P < 0.001), mean age (P < 0.001), average distance from hospital (P = 0.052), and average transfer time to the hospital (P < 0.001). We found gender differences in TRS after falling trauma (P = 0.006) when the transfer was performed by emergency medical services (EMSs) and in penetrating trauma (P < 0.001) when the transfer was performed by private vehicles. The difference in the observed characteristics of men and women explained 67% of gender differences in TRS (P = 0.06). Conclusion: The gender difference in the transfer of injured patients was in favor of men, depending on the socio-demographic and clinical factors. In OB analysis, the gender differences in falling trauma and transfer by EMS and the gender differences in penetrating trauma and private transmission to the hospital were also confirmed. Steps need to be taken to ensure that services are equally beneficial to both men and women.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".