Knowledge of Overdiagnosis and the Decision To Participate in Breast Cancer Screening
Bibliographic record
Abstract
In 2014, breast cancer was the second leading cause of death among Canadian women, with women over age 50 years making up 82% of the identified cases. To address this issue, the Ontario Breast Screening Program developed a media campaign that promoted the benefits of mammogram screening, but not the associated risks (i.e., false-positive, false-negative, radiation exposure, and overdiagnosis). This study was designed to determine whether there was a statistically significant relationship between knowledge of overdiagnosis and participation in mammogram screening. This cross-sectional, correlational study used schema theory supported by the effective health communication model. Forty-one women were invited to listen to a brief presentation on the benefits and risks of screening mammograms and then completed a modified Champion Health Belief Model Scale survey. Two sample t tests and logistic regression analyses of the survey scores showed that the data did not support any correlations with education and screening, but did indicate a correlation between overdiagnosis and participation. The less a participant felt that overdiagnosis was a negative consequence, the more likely they were to participate in breast screening. Survey participants also stated that promotions of mammograms should present balanced information about the benefits and risks of screening. The positive social change and policy implications of this study include providing women aged 50-69 years more information on overdiagnosis in mammograms so they are more informed participants in the decision-making process, and educating Ontario government policymakers with information about the barriers that women aged 50-69 years face in getting balanced information on mammography programs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".