A systematic review of management strategies for children’s mental health care in the emergency department: update on evidence and recommendations for clinical practice and research
Bibliographic record
Abstract
OBJECTIVE: Children with mental health crises require access to specialised resources and services which are not yet standard in general and paediatric EDs. In 2010, we published a systematic review that provided some evidence to support the use of specialised care models to reduce hospitalisation, return ED visits and length of ED stay. We perform a systematic review to update the evidence base and inform current policy statements. METHODS: Twelve databases and the grey literature were searched up to January 2015. Seven studies were included in the review (four newly identified studies). These studies compared ED-based strategies designed to assess, treat and/or therapeutically support or manage a mental health presentation. The methodological quality of six studies was assessed using the Cochrane Effective Practice and Organization of Care Risk of Bias tool (one interrupted time series study) and a modified Newcastle-Ottawa Scale (three retrospective cohort and two before-after studies). The Grading of Recommendations Assessment, Development and Evaluation (GRADE) system was applied to rate overall evidence quality (high, moderate, low or very low) for individual outcomes from these six studies. An additional study evaluated the psychometric properties of a clinical instrument and was assessed using criteria developed by the Society of Pediatric Psychology Assessment Task Force (well-established, approaching well-established or promising assessment). RESULTS: There is low to very low overall evidence quality that: (1) use of screening laboratory tests to medically clear mental health patients increases length of ED stay and costs, but does not increase the risk of clinical management or disposition change if not conducted; and (2) specialised models of ED care reduce lengths of ED stay, security man-hours and restraint orders. One mental health assessment tool of promising quality, the home, education, activities and peers, drugs and alcohol, suicidality, emotions and behaviour, discharge resources (HEADS-ED), has had good accuracy in predicting admission to inpatient psychiatry. CONCLUSIONS: Lower-quality data suggest benefits to the use of specialised resources and services for paediatric mental health care in general and paediatric EDs. Experimental evaluation of strategies and the inclusion of patient-reported outcomes will improve confidence in these findings. Additional psychometric studies are needed for the HEADS-ED tool to be considered well established.
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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 | 0.034 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".