Lay health educator role in improving cancer screening rates in underserved communities
Bibliographic record
Abstract
There are inequities in cancer screening among people living in one large province in Canada; these include breast, cervical and colorectal cancer screening. The use of peer support or lay health educator models is often used in promoting health behaviours to communities. This paper outlines some of the conceptual understandings of peer support and lay health educator models and describes an application of a lay health educator program called Screening Saves Lives. The program structure and activities are discussed as well as lessons learned over a period of six years. Three key theoretical perspectives support the design of the model— Health Belief Model, Stages of Change model and PRECEDE model. The program has reached over 35,000 community members within one region using laypersons who are trained in providing tailored messages on cancer screening, supporting and follow-up. Additionally, the program has been a catalyst in identifying barriers to cancer screening and enables positive changes in the health care system. Screening Saves Lives is currently being scaled to other communities in the province.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".