Optimizing Women's Health: Cancer Screening Strategies for Long-Stay Mental Health Inpatients in Canada
Bibliographic record
Abstract
Background: Individuals with severe mental illness (SMI) have significantly lower rates of cancer screening, leading to increased poor health outcomes, including death. Some of the contributing factors contributing to low screening rates include stigma as well as the lack of access to primary care services.2 As a result, there is a need to look at new systems to support this population in order to deliver more equitable care.3Currently, Ontario Health offers cancer screening programs to individuals. However, individuals with SMI have disproportionately had lower adoption of these programs compared to the general population.4 In some cases, screening rates can be 30% lower than the general population.5,6 In order to address this gap, one of the ways is to deliver cancer screening in inpatient mental health settings, where this population often receive care. This work focuses on recommendations for delivering women health and other related cancer screening to individuals with SMI in inpatient mental health settings. Approach: A number of quality improvement initiatives were conducted to explore ways of working with community partners to deliver women's health cancer screening to individuals with SMI while receiving care in inpatient settings. Using a co-design approach, models of care were developed which included building dedicated processes for referral, follow-up care and education materials. These models of care were piloted in a mental health hospital in Toronto, Ontario and the evaluations informed the recommendations that are outlined here. Results: A number of recommendations were identified from the QI initiatives. For example, patient and family education continues to be a significant gap and there needs to be facilitation of dedicated patient education sessions that is trauma-informed and considerate of their concerns. In addition, there is a need to develop dedicated workflows that provide patients and support persons a comfortable space to receive the screening (e.g., a larger screening room, more time per appointment). Subsequently, due to the lack of primary care for this population, there is a need to develop dedicated processes for follow-up care, should there be a need. By engaging with clinicians and patient and family specialists, it can help inform the development of meaningful processes, supports and partnerships for supporting cancer screening in this population. Implications: To our knowledge, this is one of the first initiatives focused on improving the delivery of cancer screening to this population. These findings can be used by health care administrators and clinicians as a starting point to adapt and develop similar initiatives to increase screening rates for individuals in their own patient population. Future work should focus on evaluating and scale-up the impact of these initiatives in other areas. References: Murphy KA, Stone EM, Presskreischer R, McGinty EE, Daumit GL, Pollack CE. Cancer screening among adults with and without serious mental illness: a mixed methods study. Medical care. 202 Apr ;59(4):327-33.2. Hope H, Pierce M, Johnstone ED, Myers J, Abel KM. The sexual and reproductive health of women with mental illness: a primary care registry study. Archives of Women's Mental Health. 2022;25(3):585-593.3. Lawley ME, Cwiak C, Cordes S, Ward M, Hall KS. Barriers to Family Planning Among Women With Severe Mental Illness. Women's Reproductive Health. 2022;9(2):00-8. doi:0.080/2329369.202.206394. Rabeneck L, Tinmouth JM, Paszat LF, Baxter NN, Marrett LD, Ruco A, Lewis N, Gao J. Ontario's ColonCancerCheck: results from Canada's first province-wide colorectal cancer screening program. Cancer epidemiology, biomarkers prevention. 204 Mar ;23(3):508-5.5. ONeill B, Yusuf A, Lofters A, Huang A, Ekeleme N, Kiran T, Greiver M, Sullivan F, Kurdyak P. Breast cancer screening among females with and without schizophrenia. JAMA network open. 2023 Nov ;6():e2345530-.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".