The Newcomer Well Woman Clinic: A Cross-Sectional Evaluation of an Innovative Care Model to Support the Sexual and Reproductive Health Needs of Refugee Women
Bibliographic record
Abstract
Abstract Background: The Newcomer Well Woman Clinic (NWWC) was developed as a partnership between primary care physicians and obstetrics and gynecology residents to ensure timely provision of sexual and reproductive healthcare for refugee patients. Since 2015, the NWWC has provided monthly clinics offering contraceptive counselling, cervical cancer screening, and intrauterine device insertions with accompanying education sessions. This study aimed to evaluate women’s experiences at the clinic and with interpretation services. Methods: A sample of patients who attended appointments at the NWWC between January 2015-December 2020 were invited to participate in a telephone survey facilitated by an interpreter to evaluate their clinic experience. The survey was adapted from two validated survey measures: the PSQ-18 and CAHPS. Survey results were reported using descriptive statistics. Clinic audit data was used to summarize patient demographics and service delivery during this period. Results: Since 2015, 288 patients have attended the clinic. Despite the COVID-19 pandemic, the number of appointments increased annually. 76% of eligible invitees consented to participate. Most patients were highly satisfied (74%) or satisfied (10%) with their care and found the education sessions helpful (80%). Frequently requested education topics included cervical cancer prevention (54%) and contraception (36%). 80% of patients used an interpreter at the clinic and 88% felt that their concerns were always conveyed appropriately to the physician. Conclusion: Refugee patients were highly satisfied by the sexual and reproductive healthcare provided by the NWWC and with interpretation services. To meet the needs of refugees, innovative care models such as the NWWC could be adopted by healthcare systems.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".