Socioeconomic Characteristics, Lifestyle Behaviors, and Health Conditions Among Males of Reproductive Age With and Without Disabilities, NHANES 2013–2018
Bibliographic record
Abstract
Health status during the reproductive years influences fecundity, fertility, and the future health of males and their offspring. There remains a dearth of literature examining men's preconception health, especially among high-risk populations, such as those with disabilities. The objective of this study was to examine indicators of preconception health, including chronic medical conditions, lifestyle behaviors, and health care utilization, among males of reproductive age with and without disabilities in the United States. Data were from 3,702 males of reproductive age (18-44 years) who participated in the National Health and Nutrition Examination Surveys, 2013-2018. Approximately 14% of males reported having at least one disability related to vision, hearing, cognition, mobility, self-care, or independent living. Among all men, suboptimal preconception health indicators were prevalent including poor or fair self-rated health; low education and household income status; lack of health insurance and no recent utilization of health care and dental care; cigarette smoking; frequent alcohol consumption and binge drinking; marijuana and illegal drug use; obesity; low fruit and vegetable intake and no multi-vitamin use; low physical activity; short sleep durations; depressive symptoms; and hypertension and asthma. Compared to males with no disabilities, males with any disabilities were more likely to have suboptimal preconception health indicators. Strategies to promote and improve sexual health, preconception care, and family planning services among all men are needed. For males with disabilities, specifically, further investigation of their specific health needs related to sex, reproduction, family planning, and fatherhood, as well as interactions with health care providers, is required.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".