Decisional Needs of African American Smokers for Lung Cancer Screening
Bibliographic record
Abstract
The burden of lung cancer is significant for African Americans, especially African American men, who have the highest lung cancer death rates compared to all racial and ethnic groups. In 2013, the United States Preventive Services Task Force (USPSTF) provided a recommendation for annual screening using low-dose computed tomography (LDCT) due to the disease-specific mortality benefit. Given the test’s limitations (e.g., false positives, radiation exposure) the USPSTF and other medical organizations endorse informed decision-making, a process by which individuals weigh the benefits and harms of the test and make a decision about usage. The recent release of the screening guidelines and promotion of informed decision-making provides a timely opportunity to examine patients’ decision-making processes around lung cancer screening. The purpose of this study was to describe aspects of decision-making for lung cancer screening including knowledge and awareness about LDCT, values related to screening, uncertainty about the test, and decision-making preferences among African American adult smokers. Additionally, this study examined the extent to which decision-making components are associated with screening intentions. The Ottawa Decision Support Framework provided the conceptual framework for this study, positing that patients’ decisional needs (e.g., knowledge, personal values) impact decisional quality (e.g., being informed, low regret) which ultimately influences behavior (e.g., use of health services). First, patient and provider key informant interviews (N=9) were conducted to inform the development of a decisional values measure. Next, a survey was administered to African American (N=119) long-term smokers. Among the study sample, lung cancer screening awareness and knowledge were limited (Mean=7.1 out of 15). Individuals were experiencing uncertainty about the screening decision, but lower decisional conflict was associated with greater likelihood of talking with friends about LDCT as well as screening intention (p’s
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".