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Enregistrement W4413358469 · doi:10.5334/ijic.nacic24045

Combining digital tools in primary care with community outreach, a co-designed approach for preventative cancer screening in East Toronto

2025· article· en· W4413358469 sur OpenAlexaboutno aff
Rishma Pradhan, Iman Khan, Jeff Powis, Anne Wojtak, Karen Chu, Jen Quinlan

Notice bibliographique

RevueInternational Journal of Integrated Care · 2025
Typearticle
Langueen
DomaineHealth Professions
ThématiqueMobile Health and mHealth Applications
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOutreachPrimary careCancer screeningMedicineNursingFamily medicineCancerPolitical science

Résumé

récupéré en direct d'OpenAlex

Background: East Toronto Health Partners (ETHP) Ontario Health Team serves 400,000 people, with a population consisting of 50% immigrants. There is significant variation in cancer screening rates across our 2 different neighbourhoods, in which neighbourhoods with higher rates of newcomers and lower-socioeconomic status have 5-25% lower cancer screening rates. Analysis shows the key barriers for our underserved populations include difficulty accessing services, language constraints, and lack of knowledge about the importance of screening. Demographics and culture also play a role in health practices and there is a need to address misinformation about screening. Approach: In 2023, ETHP worked with eHealth Center of Excellence to create and launch;Poppy-bot- a robotic process automation tool that uses primary care electronic medical records (EMRs) to identify and segment people who are due or overdue for screening. With an equity perspective we segmented people by postal code and language in order to build targeted outreach and access to screening. In East Toronto, Poppy-bot has identified over 5,000 people for screening.Flemingdon Health Centre (FHC), is located in the middle of two underserved neighbourhoods with lower cancer screening rates. FHC is also the lead agency for our multi-agency, peer-based, community health ambassadors (CHA) program, which hires local residents from equity-deserving neighbourhoods to act as peer support and outreach workers. These CHAs are a highly effective at bridging the gaps that disproportionately impact our communities.ETHP was inspired to combine the strengths of two separate initiatives to address cancer screening rates by ) using Poppy-bot to flag people due for cancer screening with 2) our CHAs to conduct targeted outreach to residents in communities with lower cancer screening rates.Given the cultural sensitivity needed for outreach to different communities, we worked with CHAs and community members to co-design educational material. Through focus groups and workshops, we leveraged behavioral science, conversational receptiveness and motivational interviewing. Engaging consultants from Behavioural Insights Team and Health Commons Solution Lab, we co-designed a Cancer Screening Guide used by the CHAs to have conversations with community members in Arabic, Bengali, Dari, Pashto, Slovak, and Urdu.Using lists of individuals generated by Poppy-bot and segmented by language, CHAs had 5 training sessions around communications and cultural competency and used the guide to call individuals to discuss the importance of cancer screening. CHAs explained how to get a screening completed, what to expect at the screening and how to interpret results when they are available. Access to nursing staff was available if the CHAs needed. Results: The team received an innovation award (from MARS Canadian Cancer Society) which helped fund the creation of the Cancer Screening Guide, which will be shared for use in other jurisdictions. This project demonstrated the success of combining digital tools in primary care with community outreach to make an impact to local communities. Over a month period, CHAs had one-to-one conversations with 375 individuals. Implications: ETHP further learned the value of co-designing outreach and education materials with community members to help shape and build programming.While the calls were focused on cancer screening, residents also had other health-related questions. The learning from this process is enabling us to further develop the CHA program, including understanding barriers to access for community members and how to support difficult conversations about health issues within our diverse communities.Person contacted by a CHA testimonial: It was helpful to be reminded of the fact that I was due for screening. They talked to me about the specific test I needed and answered my questions. Usually other doctors offices don't even call you or tell you that you are due, so I appreciated it.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,585
Score d'incertitude au seuil0,825

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,005
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0050,002
Communication savante0,0030,001
Science ouverte0,0020,005
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0100,001

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.

Tête enseignante Opus0,063
Tête enseignante GPT0,442
Écart entre enseignants0,379 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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