Promising practices used by International Medical Graduate Physicians to increase cervical screening in South Asian and Chinese immigrants in the Greater Toronto Area
Bibliographic record
Abstract
Cervical cancer is one of the most preventable cancers, with Pap tests being a widely accessible form of screening throughout Canada. However, participation in cervical cancer screening is historically lower in South Asian and Chinese immigrants. Previous literature showed that the risk of being under-screened is even higher for these women if they receive care from a provider that is from a culturally congruent region. This investigation aimed to qualitatively explore this phenomenon through the perspective of South Asian and Chinese International-Medical Graduate (IMG) physicians. Semi-structured interviews were conducted to identify barriers to cervical screening faced by South Asian and Chinese immigrants across the Greater Toronto Area (GTA), as well as promising practices that are currently being used by IMG physicians to increase cervical screening participation among their patients. Several barriers to and interventions designed to screening were identified at the individual, community, and health care provider level. Promising interventions include linguistically and culturally appropriate health education and access to a female provider.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".