Developing a predictive model for prostate cancer screening intent among African American males
Bibliographic record
Abstract
The purpose of this cross-sectional, predictive correlational study was to examine the relationship between African American male inmates’ behavioral intentions with regard to the intention to screen for prostate cancer. To this end, the study devised and tested a model of relevant predictors, including Direct Attitude, Indirect Subjective Norms, Indirect Outcome Evaluation, and Health Literacy. Data were analyzed using descriptive and inferential statistics. The findings suggest that African American male inmates in the federal prison system may have slightly different priorities relative to non-incarcerated populations. The implications for nurses and other healthcare providers working in the prison system include: empowering inmates by building a trusting relationship; investigating ways to improve health literacy in this population, and developing an understanding of the factors that inspire African American inmates to engage in the decision-making process. The limitations of this study include a reliance on participant self-reports and a relatively small sample size, which limit the generalizability of the results. Nonetheless, future interventions may arise from providers’ greater ability to understand and predict health-related behaviors and foster proactive health attitudes in the inmate population.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".