Addressing Health Inequities in Action: Population Health Improvements in Cervical Screening
Bibliographic record
Abstract
Background: With one of the lowest cervical screening rates in the province, the population served by the Central West Ontario Health Team is culturally diverse with a high unattachment rate to comprehensive primary care. With a rate of 54.3% of eligible people being screened for cervical cancer in 209 the rate of screening dropped to 46.7% during the pandemic. The barriers to screening were reviewed from several perspectives including research, analyzing population health data and community-based engagement activities by holding focus groups. The key barriers to screening were summarized as: structural access, cultural, religious, socioeconomic elements. Approach: As part of the Central West Ontario Health Team Collaborative Quality Improvement Plan two strong Community Health Centres (CHC) in the OHT, WellFort CHS and Rexdale CHC partnered together to commit to improve cervical screening rates.By first utilizing population health data the partners identified the most at-risk communities by postal code zoning. Then the partners committed to owning that postal code zone to improve the rate of cervical screening of eligible individuals in that geography. The quality aim statement was as follows: Increase cervical cancer screening rate by 8% of eligible clients living in FSA-L4T, L6T, L6V, M9V by March 2024. At a population health level we aimed to improve cervical screening by .0% at a whole population health level.Significant community participation occurred by partnering with differing cultural and neighbourhood groups through a cultural competency lens. Focus groups were held to identify the specific barriers to screening and the ideal strategies to address the barriers. The benefits of the CHC action of the model of health and well being was uniquely demonstrated during this project with a focus on health equity, capturing and analyzing the social determinants of health, community engagement and standardizing data management and care approaches. Results: Once the barriers and improvement opportunities to care were identified the teams worked together to embed community health ambassadors into the key communities to build trust and improve health literacy. Following this our primary care teams followed to deliver the services in community centric and mobile health approaches. The dedicated and community centric outreach activities saw 3,000 interactions with the targeted population over the initiative. Screening approaches continued to be informed by these deep connections with citizens in our communities and it allowed our teams to have trusted relationships that increased readiness for cervical screening in the identified neighbourhoods.This intervention saw our teams conduct 50% more cervical screening than our target and we exceeded our 8% target in each postal code neighbourhood and in the lowest screened neighbourhood of the province we improved the screening rate by 24% in this one year. The population health rate reported at an OHT level in September of 2023, had already seen a 3% population health rate improvement of which we believe this initiative had a strong impact on. Implications: Learnings include both successes and opportunities that increase as the scale of population health systems of care are analyzed and leveraged in practice. Leveraging the CHC model of care and experience in this area was noted as a key strength in the population health approach.Increased competency building for point of care staff in areas of quality improvement, system thinking, population health and collection and analyzation of social determinants of health were a few key areas of success.The most profound element of success however was the client and community focus that allowed our model to challenge systemic barriers impacting access to care and culturally appropriate care and drove the execution of our implementation of care.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".