Evidence map›Paper›PMID 40922766›Full record

ArticleJAACAP open2025

Electronic Health Record-Integrated Legal Documentation to Measure Involuntary Mental Health Detention of Children.

Juliet Beni Edgcomb, Chi-Hong Tseng, Alexandra M Klomhaus, Ariel Seroussi, Jonathan P Heldt, Chrislie G Ponce, Liliana Perez, Joshua J Lee, Bonnie T Zima

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In one paragraph

Article in JAACAP open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Juliet Beni EdgcombUniversity of California, Los Angeles, Los Angeles, California.
Chi-Hong TsengUniversity of California, Los Angeles, Los Angeles, California.
Alexandra M KlomhausUniversity of California, Los Angeles, Los Angeles, California.
Ariel SeroussiUniversity of California, Los Angeles, Los Angeles, California.
Jonathan P HeldtUniversity of California, Los Angeles, Los Angeles, California.
Chrislie G PonceUniversity of California, Los Angeles, Los Angeles, California.
Liliana PerezUniversity of California, Los Angeles, Los Angeles, California.
Joshua J LeeUniversity of California, Los Angeles, Los Angeles, California.
Bonnie T ZimaUniversity of California, Los Angeles, Los Angeles, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To examine the prevalence and correlates of child involuntary mental health detentions through evaluation of legal documentation embedded in medical records and children's electronic health information. Method: Medical records were analyzed from 3,440 children ages 10 to 17 years with MH-related emergency department visits in a large academic health system over 2 years (2017-2019). Bivariate analyses and random forests were deployed to identify child-, neighborhood-, and systems-level correlates of involuntary MH detentions. Results: Nearly 1 in 4 (n = 769, 22.4%) visits involved an involuntary detention. Half of detained children (n = 357, 46.4%) arrived on a detainment that was discontinued after MH provider evaluation. Odds of detention were greater among Black (odds ratio 1.33 [95% CI 1.02-1.73]) and publicly insured (odds ratio 1.63 [95% CI 1.37-1.94]) children. Children detained in prehospital settings resided in census tracts with greater social vulnerability scores (χ Conclusion: Medical record-embedded legal documentation supports transparency in the use of detentions, which are common and jointly predicted by children's clinical need and social vulnerability.

Indexed as

adolescentchildelectronic health recordsemergenciesmental health

Identifiers

PMID40922766
PMCPMC12414314

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.