Evidence map›Paper›PMID 34467772›Full record

ArticleAssessment2023

Perceived Causal Problem Networks: Reliability, Central Problems, and Clinical Utility for Depression.

Lars Klintwall, Martin Bellander, Matti Cervin

Abstract read
In one paragraph

Article in Assessment, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–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

16 citing papers in PubMed.

  1. Article
  2. Article
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  5. Article
  6. Parental Perceived Causal Networks of Problematic Screen Time in Adolescents: The Centrality of Passive Scrolling.Scandinavian journal of child and adolescent psychiatry and psychology · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
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  12. Predicting the outcome of psychotherapy for chronic depression by person-specific symptom networks.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2024
    Article
  13. Article
  14. Article
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  16. Article
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 KlintwallStockholm University. Stockholm, Sweden.ORCID 0000-0003-4931-6033
Martin BellanderKarolinska Institute, Stockholm, Sweden.
Matti CervinLund University, Lund, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Personalized case conceptualization is often regarded as a prerequisite for treatment success in psychotherapy for patients with comorbidity. This article presents Perceived Causal Networks, a novel method in which patients rate perceived causal relations among behavioral and emotional problems. First, 231 respondents screening positive for depression completed an online Perceived Causal Networks questionnaire. Median completion time (including repeat items to assess immediate test-retest reliability) was 22.7 minutes, and centrality measures showed excellent immediate test-retest reliability. Networks were highly idiosyncratic, but worrying and ruminating were the most central items for a third of respondents. Second, 50 psychotherapists rated the clinical utility of Perceived Causal Networks visualizations. Ninety-six percent rated the networks as clinically useful, and the information in the individual visualizations was judged to contain 47% of the information typically collected during a psychotherapy assessment phase. Future studies should individualize networks further and evaluate the validity of perceived causal relations.

Indexed as

Cognitive DysfunctionDepressionHumansPsychotherapyReproducibility of Resultscase conceptualizationCBTcognitive behavioral therapycomorbiditydepressionnetwork analysisnosology

Identifiers

PMID34467772
PMCPMC9684655

What OpenQuestion holds

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Registered trials

None linked

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.