Improving dual diagnosis care in acute psychiatric inpatient settings through education
Bibliographic record
Abstract
Background Patients with co-existing substance use and mental disorder (dual diagnosis) have complex and challenging care needs. Acute psychiatric care settings play a vital role in providing services for patients with dual diagnosis as they often do not voluntarily seek treatment. This is significant in that recent data reveals that 57% of the psychiatric inpatients at an inner city hospital in Vancouver, Canada are characterized as dual diagnosis. Purpose To develop an educational module which will equip nurses/practitioners with the skills and knowledge required to deliver evidence-based dual diagnosis care in acute psychiatric settings. Methods A survey of 74 nurses working in acute psychiatric settings was completed to identify their learning needs and challenges. This was followed by a comprehensive review of evidence from literature to identify competencies, knowledge and skills needed to deliver dual diagnosis care. Content for the educational module was then validated by a panel of leading international experts on dual diagnosis. Two focus groups of acute psychiatric nurses were then conducted to discuss content. An 8 hour educational session was then developed and piloted using the content that was reviewed and validated. Results Thirteen content areas were identified and validated by experts. Evaluations from participants of the educational session suggest improved knowledge, skills and competencies in dual diagnosis care. Conclusions This project translates evidence into practice, contributes to the body of knowledge on dual diagnosis care and improves practitioners’ confidence and competency in delivering evidence-based care which also will improve patient care outcomes and experiences. Disclosure of interest The author has not supplied his declaration of competing interest.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".