Interventions to improve emergency department use for mental health reasons: protocol for a mixed-methods systematic review
Bibliographic record
Abstract
BACKGROUND: Healthcare resources are limited and unnecessary, and inappropriate emergency department use is now a highly visible healthcare priority. Individuals visiting the emergency department for mental health-related reasons are often amongst the most frequent presenters. In response, researchers and clinicians have created interventions to streamline emergency department use and several primary studies describe the effects of these interventions. Yet, no consensus exists on the optimal approach, and information on the quality of development, effectiveness, acceptability, and economic considerations is hard to find. The purpose of this study is to systematically review interventions designed to improve appropriate use of the emergency department for mental health reasons. METHOD: A mixed-method systematic review using Joanna Briggs Methodology. Search combining electronic databases (EMBASE, MEDLINE, PsycINFO, CINAHL, HealthSTAR, PROQUEST, Cumulative Index to Nursing and Allied Health) and secondary searches (grey literature and hand search with consultation). Two independent reviewers will screen titles and abstracts using predetermined eligibility criteria and a third reviewer will resolve conflicts. Full texts will also be screened by two independent reviews and conflicts resolved in a consensus meeting with a third reviewer. A pilot-tested data extraction form will be used to retrieve data relevant to the study objectives. We will assess the quality and of all included studies. Data describing interventions will be summarized using logic models and reported narratively. Quality of development will be assessed using the Oxford Implementation Index. For data on intervention effectiveness, we will assess statistical heterogeneity and conduct a meta-analysis using a random effects method, if appropriate. For interventions that cannot be pooled, we will report outcomes narratively and descriptively. Qualitative data on acceptability will be synthesized using meta-aggregation and an economic evaluation of interventions will be done. The reporting of this protocol follows the PRISMA-P statement. DISCUSSION: Using a combined systematic review methodology and integrated knowledge translation plan, the project will provide decision makers with concrete evidence to support the implementation and evaluation of interventions to improve emergency department use for mental health reasons. These interventions reflect widespread priorities in the area of mental health care. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018087430.
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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 | 0.113 | 0.109 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.021 | 0.023 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.077 | 0.011 |
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".