Perceptions of prevalence, consequences, and strategies for managing contraband substance use in an inpatient concurrent disorders program: A qualitative study of patient perspectives and survey of clinician perspectives
Bibliographic record
Abstract
Objective: Inpatient treatment programs for substance use disorders (SUDs) typically have an abstinence policy for patients, but unsanctioned substance use nonetheless takes place and can have significant negative clinical impacts. The current study sought to understand this problem from a patient perspective and to develop strategies for improved contraband substance management in an inpatient concurrent disorders sample. Methods: = 10; 60% female) was undertaken to ascertain perceived prevalence, impact, and patient-generated strategies. Second, an anonymous follow-up survey was conducted with unit staff clinicians to evaluate the suggested strategies. Results: Patients reported that contraband substance use was present and had significant negative consequences clinically. Recommendations from patients included more extensive urine drug screening, the use of drug-sniffing dogs, and direct contingencies for contraband use. Nineteen staff competed an anonymous follow-up questionnaire to evaluate the viability of these strategies, revealing variable perceptions of feasibility and effectiveness. Conclusion: These findings emphasize the adverse consequences of contraband substance use in addiction treatment programs and identify patient-preferred strategies for managing this challenge.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".