Health Status and Preventive Health Services Among Reproductive-Aged Women in Treatment for Opioid Use Disorder
Bibliographic record
Abstract
Objective: To assess the utilization of preventive health services and the prevalence of chronic health conditions among a cohort of women in treatment for opioid use disorder (OUD). Methods: Ninety-seven women who were receiving treatment for OUD from a single urban treatment program completed a self-administered anonymous online questionnaire that asked about demographics, health, receipt of preventive health services, and utilization of health care. Descriptive statistics were used to describe data. Results: More than one-third of respondents reported that their health was fair or poor, whereas one-quarter were very concerned with their health. Most participants (59%) reported at least one chronic health condition; nearly 1 in 5 reported two or more conditions. Less than half of respondents had received a routine medical examination in the past year. Vaccine uptake was low; 56% received the coronavirus disease 2019 vaccine and 36% received the annual influenza vaccine. Conclusions: Women in treatment for OUD could benefit from enhanced health care to address the high rates of chronic diseases and risk factors and underutilization of recommended preventive health services. Interventions and models of care that aim to enhance utilization of such services, and ultimately improve the health of this vulnerable population, may be worth exploring.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".