The influence of undertreated chronic pain in a national survey: Prescription medication misuse among American indians, Asian Pacific Islanders, Blacks, Hispanics and whites
Bibliographic record
Abstract
OBJECTIVE: Disparities in the assessment and treatment of chronic pain among racial/ethnic may lead to self-treatment for undertreated pain. This study examines whether pain intensity among US racial/ethnic groups' influences rates of psychotherapeutic prescription drug misuse. METHODS: Data included civilian, non-institutionalized adults (age 18-99 years) residing in the United States (n = 34,653) from Waves 1 and 2 of the National Epidemiological Survey on Alcoholism and Related Conditions (NESARC; 2004-2005). The primary outcome variable was prescription drug misuse/PDM (i.e., use without a prescription or other than as prescribed) including tranquilizers, sedatives, stimulants, or opioids. Predictor variables included self-reported race/ethnicity (American Indian, Black, Hispanic, or White) and pain intensity. Data were analyzed in 2019. RESULTS: (1) = 0.65, p = 0.42). PDM rates for Black participants remained lowest of all other racial/ethnic groups and plateaued with increasing pain intensity. CONCLUSIONS: Our results indicate that undertreated chronic pain may drive rates of PDM among varying racial/ethnic groups. Providing equitable assessment and treatment of pain intensity remains critical. Additional research is needed to examine provider decision-making and unconscious bias, as well as patient health beliefs surrounding perceived need for prescription pain medications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".