Evidence map›Paper›PMID 24506810›Full record

ArticleBMC health services research2014

Local psychiatric beds appear to decrease the use of involuntary admission: a case-registry study.

Lars Henrik Myklebust, Knut Sørgaard, Rolf Wynn

Abstract read
In one paragraph

Article in BMC health services research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  10. Predicting involuntary hospitalization in psychiatry: A machine learning investigation.European psychiatry : the journal of the Association of European Psychiatrists · 2021
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  13. Factors associated with involuntary psychiatric hospitalization in Portugal.International journal of mental health systems · 2021
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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

3 authors.

Lars Henrik Myklebust
Knut Sørgaard
Rolf WynnDepartment of Clinical Medicine, University of Tromsø, Tromsø N-9037, Norway. rolf.wynn@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStudies on the effect of organizational factors on the involuntary admission of psychiatric patients have been few and yielded inconclusive results. The objective was to examine the importance of type of service-system, level of care, length of inpatient stay, gender, age, and diagnosis on rates of involuntary admission, by comparing one deinstitutionalized and one locally institutionalized service-system, in a naturalistic experiment.

methods5538 admissions to two specialist psychiatric service-areas in North Norway were studied, covering a four-year period (2003-2006). The importance of various predictors on involuntary admission were analyzed in a logistic regression model.

resultsInvoluntary admission to the services was associated with the diagnosis of psychosis, male sex, being referred to inpatient treatment, as well as type of service-system. Patients from the deinstitutionalized system were more likely to be involuntarily admitted.

conclusionsSeveral factors predicted involuntary status, including male sex, the diagnosis of psychosis, and type of service-system. The results suggests that having psychiatric beds available locally may be more favourable than a traditional deinstitutionalized service system with local outpatient clinics and central mental hospitals, with respect to the use of involuntary admission.

Indexed as

AdultAgedAge FactorsCommitment of Persons with Psychiatric DisordersFemaleHumansLength of StayLogistic ModelsMaleMental DisordersMental Health ServicesMiddle AgedNorwaySex FactorsYoung Adult

Identifiers

PMID24506810
PMCPMC3923252

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Registered trials

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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.