Enhancing nurses' capacity to provide concurrent mental health and substance use disorder care: A quasi-experimental intervention study
Bibliographic record
Abstract
BACKGROUND: Patients experiencing concurrent disorders (i.e., co-occurring mental health and substance use disorders) are prevalent in mental health settings and their health and social outcomes are often poor. This reflects persistent stigma as well as inadequate preparatory training or continuing education for healthcare professionals, including nurses. OBJECTIVE: To explore the impacts of the 1-day 'Enhancing Concurrent Disorder Care Intervention' on nurses' and student nurses' capacity to deliver care, grounded in current evidence, to patients with concurrent disorders in inpatient mental health settings. DESIGN: -test components, guided by the STROBE checklist for observational studies. SETTINGS: Five acute mental health units across two hospitals in British Columbia, Canada, as well as two schools of nursing representing students completing clinical practicum rotations within these settings. PARTICIPANTS: Seventy-six nurses (Registered Nurses and Registered Psychiatric Nurses) and student nurses practicing in inpatient mental health care. METHODS: This educational intervention was informed by a pilot study, which included content validation from international concurrent disorder experts, and further refined through collaborative processes with lived experience and nurse partners. Intervention impacts were examined using online surveys conducted prior to the intervention and within two weeks post-intervention. Surveys assessed knowledge and attitudes about concurrent disorders using a validated instrument and questions developed by the study team. Descriptive statistics alongside paired and independent t-tests and two-way ANOVAs were used to compare survey scores before and after the intervention. RESULTS: Findings indicate that the intervention was effective in improving participants' knowledge and attitudes toward patients with concurrent disorders across participant groups. CONCLUSIONS: Enhancing care and outcomes for patients with concurrent disorders is a global priority. Brief educational interventions aimed at nurses can provide an effective, low-barrier mechanism to address knowledge gaps that contribute to harmful care and adverse outcomes.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".