Optimizing Women's Health: Cancer Screening Strategies for Long-Stay Mental Health Inpatients in Canada
Notice bibliographique
Résumé
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-.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».