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Record 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 on OpenAlexaboutno aff
Rishma Pradhan, Iman Khan, Jeff Powis, Anne Wojtak, Karen Chu, Jen Quinlan

Bibliographic record

VenueInternational Journal of Integrated Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsOutreachPrimary careCancer screeningMedicineNursingFamily medicineCancerPolitical science

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.825

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0030.001
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.063
GPT teacher head0.442
Teacher spread0.379 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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