Do Patients in a Primary Care Practice Know the Current Cancer Screening Guidelines?
Bibliographic record
Abstract
Background: In spite of supporting evidence and widespread promotional campaigns, screening rates for breast, cervical and colorectal cancers in Ontario are lower than expected. These low screening rates may be partially due to lack of knowledge on the part of patients. Given the importance of early detection to reduce cancer mortality and morbidity, it is prudent to investigate where knowledge deficits may exist. The purpose of this study was to assess patient knowledge of the Ontario screening guidelines for breast, cervical and colorectal cancers. Methods: Patients of a family health team in Toronto, Ontario were surveyed regarding their knowledge of cancer screening guidelines. Questions included knowledge regarding the test, screening interval and age for cancer screening for breast, cervical and colorectal cancers as well as sociodemographic characteristics. Responses were summarized using descriptive statistics. Results: A total of 117 patients were surveyed. Knowledge of the appropriate screening test was high for breast and cervical cancer (85.5% and 70.1% respectively) though much lower for colorectal cancer (17.1%). Knowledge regarding the age that screening should occur and the screening intervals were much lower across all cancer types. For breast cancer, 16.2% knew the age screening should occur and 30.8% knew the screening interval. For cervical cancer, 6.8% knew the age screening should occur and only 4.3% knew the screening interval. For colorectal cancer, 32.5% knew the age to start screening and 26% knew the screening interval. Conclusions: Knowledge of the cancer screening guidelines appeared to be low across all cancer types, particularly for the ages at when screening should occur and the appropriate screening intervals. These results suggest that public health practitioners and cancer prevention organizations may need to increase efforts for patient education on cancer screening.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".