Motives for prescription opioid use: The role of alexithymia and distress tolerance
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Prescription opioid (PO) use disorder is a national public health crisis. Distress tolerance and alexithymia are two separate but related components of emotion regulation that are known to impact substance use disorders. No studies to date, however, have examined the role of distress tolerance and alexithymia in PO use disorder. Thus, the current study examined the association between distress tolerance, alexithymia, and specific motivations for PO use. METHODS: Participants were non-treatment-seeking individuals with current PO use disorder (N = 81; average age = 35.0). Assessments included the Distress Tolerance Scale, Toronto Alexithymia Scale, and the Inventory of Drug Taking Situations. RESULTS: The findings indicate that distress tolerance mediated the association between alexithymia and PO use in negative situations. Specifically, distress tolerance mediated the association between alexithymia and unpleasant emotions, testing personal control, and conflict with others. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: The results provide novel information regarding emotional states that may contribute to PO use and are malleable intervention targets. Additionally, this study adds to existing literature exploring the relationship between distress tolerance and substance use and is the first to expand upon the connection between alexithymia and distress tolerance in an opioid-using population. Implications for clinical practice and future research are discussed.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".