Implementation of three innovative interventions in a psychiatric emergency department aimed at improving service use: a mixed-method study
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) use is often viewed as an indicator of health system quality. ED use for mental health (MH) reasons is increasing and costly for health systems, patients, and their families. Patients with mental disorders (MD) including substance use disorders (SUD) and suicidal behaviors are high ED users. Improving ED services for these patients and their families, and developing alternatives to ED use are thus key issues. This study aimed to: (1) describe the implementation of three innovative interventions provided by a brief intervention team, crisis center team, and family-peer support team in a Quebec psychiatric ED, including the identification of implementation barriers, and (2) evaluate the impacts of these ED innovations on MH service use and response to needs. METHOD: Using mixed methods with data triangulation, the implementation and impact of the three above-named ED interventions were studied. Quantitative data were collected from 101 participants (81 patients, 20 family members) using a user questionnaire and patient medical records. Qualitative data were gathered from focus groups (n = 3) with key intervention staff members (n = 14). The user questionnaire also included open-ended questions. Descriptive, comparative and content analyses were produced. RESULTS: Key implementation issues were identified in relation to system, organizational and patient profiles, similar to results identified in most studies in the ED implementation literature aimed at improving responsiveness to patients with MD. Results were encouraging, as the innovations had a significant impact for improved patient MH service use and adequacy of care. Services also seemed adapted to patient profiles. Family members were grateful for the help received in the ED. CONCLUSIONS: Before implementing innovations, managers need to recognize the basic issues common to all new healthcare interventions: the need for staff training and strong involvement, particularly among physicians, development of collaborative tools especially in cases of potential cultural clash between staff and organizations, and continuous quality assessment. Future research needs to confirm the pertinence of these interventions, especially use of family-peer support teams in ED, as a highly innovative intervention. Broader ED strategies could also be deployed to improve MH services and decrease ED use for MH reasons.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".