Clinical Barriers to Effective Pharmacotherapy in Co-occurring Psychiatric and Substance Use Disorders
Bibliographic record
Abstract
Prescribing medications to patients with cooccurring psychiatric and substance use disorders often evokes distressing emotional responses from both clinician and patient that affect the delivery of appropriate pharmacological treatment. One important polarization revolves around the clinician under-prescribing to avoid feeling like he or she is overmedicating the patient versus over-prescribing when risk levels are minimized. A case report illustrates some common, rapidly shifting responses to both medication and clinician. These reactions include 1) an idealized, passive relation to the medication followed by disappointment in its weakness, 2) minimizing the danger of medication through idiosyncratic and potentially dangerous overuse to replicate effects of the addictive substance, or 3) experiencing the medication as harmful, leading to phobic avoidance and underutilization. The recommended clinical response is to avoid these polarizations and to engage with the patient's suffering and dangerous behavior by 1) taking reasonable pharmacological risks, 2) establishing provisions for safe use and frequent monitoring, 3) conveying tolerance for idiosyncratic use within safe limits, 4) regular exploration of the meaning of the medication with links to both the addiction history and the treatment relationship, and 5) frequent psychoeducation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".