Community-Based Settings and Sampling Strategies: Implications for Reducing Racial Health Disparities Among Black Men, New York City, 2010–2013
Bibliographic record
Abstract
INTRODUCTION: Rates of screening colonoscopies, an effective method of preventing colorectal cancer, have increased in New York City over the past decade, and racial disparities in screening have declined. However, vulnerable subsets of the population may not be reached by traditional surveillance and intervention efforts to improve colorectal cancer screening rates. METHODS: We compared rates of screening colonoscopies among black men aged 50 or older from a citywide random-digit-dial sample and a location-based sample focused on hard-to-reach populations to evaluate the representativeness of the random-digit-dial sample. The location-based sample (N = 5,568) was recruited from 2010 through 2013 from community-based organizations in New York City. Descriptive statistics were used to compare these data with data for all black men aged 50 or older from the 2011 cohort of the Community Health Survey (weighted, N = 334) and to compare rates by community-based setting. RESULTS: Significant differences in screening colonoscopy history were observed between the location-based and random-digit-dial samples (49.1% vs 62.8%, P < .001). We observed significant differences between participants with and without a working telephone among the location-based sample and between community-based settings. CONCLUSIONS: Vulnerable subsets of the population such as those with inconsistent telephone access are excluded from random-digit-dial samples. Practitioners and researchers should consider the target population of proposed interventions to address disparities, and whether the type of setting reaches those most in need of services.
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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 | 0.031 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".