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 im-portance 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 cha-racteristics. Responses were summarized using descriptive statistics. Results: A total of 117 pa-tients were surveyed. Knowledge of the appropriate screening test was high for breast and cer-vical 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
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 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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".