Behavioral Correlates for Quitting Opioids among Opioid-Dependent Pregnant and Non-Pregnant Women of Childbearing Age in Rural Appalachia
Bibliographic record
Abstract
Background: The opioid epidemic is particularly worrisome in the pregnant population, wherein concerns are raised about the health of a mother and her child, resulting in an alarming incidence and prevalence of Neonatal Abstinence Syndrome (NAS). The 2016 National Survey on Drug Use and Health (NSDUH) show the rate of illicit psychoactive substance use among the females aged 12 or older was 15.5% in the past year. Among pregnant women aged 15 to 44, 6.3% were illicit psychoactive substance users. In Tennessee, the number of hospital discharged NAS cases from 2002 to 2013 increased from 1.50 to 16.6 cases per 1,000 live births. This number is triple the national incidence of NAS cases over the same time period. Between 2013 and 2016, at least 52.5% of children diagnosed with NAS in Tennessee have had exposure to one prescription drug, while 27.2% were exposed to a combination of prescribed medications and illicit substances. We examined the behavioral correlates that determine the wish to quit opioids or not to quit opioids among opioid-dependent pregnant and non-pregnant women in rural Appalachia. Methods: Ten women of childbearing age, whether pregnant or not, who were receiving prescribed opioids, were recruited to join the study. All the participating women were also receiving physician-managed Medication Assisted Treatment (MAT) therapy for the treatment of severe opioid use disorder, or are currently being prescribed an opioid medication. Study variables included age, Hamilton Depression Rating Scale (HAM-D), Visual Analogue Scale – Pain (VAS-P), the Modified Opiate Craving Scale (MOCS), the Visual Analog Commitment to Quit Opiates, the McGill Pain Index (MPI), prescriptions, tobacco and nicotine use, illicit substance use, the Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES), and the Adverse Childhood Experience (ACE) questionnaire. The HAM-D, MOCS, MPI, and SOCRATES scores were log-transformed to approximate a normal distribution. Descriptive statistics and the Spearman’s rank correlation (with a 95% Confidence Interval) were conducted to examine significant behavioral correlates for quitting opioids. Results: Descriptive statistics show that women with higher HAM-D and MOCS scores are not likely to express willingness to quit opioids. There is a statistically significant strong positive correlation of 0.679 (pppp Conclusion: Women who recognize the need to quit opioids or are “taking steps” to quit are more likely to quit opioids. Women with high depression and pain scores are not likely to quit opioids. Non-opioid medications may reduce the number of opioid-dependent pregnant and non-pregnant women of childbearing age, and, in turn, lower the currently high incidence and prevalence rates of NAS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".