Evidence map›Paper›PMID 27505622›Full record

ArticleJournal of abnormal psychology2016

Identifying highly influential nodes in the complicated grief network.

Donald J Robinaugh, Alexander J Millner, Richard J McNally

Open access · greenAbstract read
In one paragraph

Article in Journal of abnormal psychology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 738 papers.

0numbers the graph read from it
0cells of the map it votes in
738citing papers in PubMed
30.8field-weighted citation impact, top 1% of its field
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

738 citing papers in PubMed, 1,574 citations in OpenAlex.

  1. Trial
  2. Mechanisms of simplified sitting Badunjin effects on symptom burden: a network-based cross-lagged analysis in advanced cancer patients.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
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  15. Comparing Depression and Anxiety Symptom Networks in Chinese Students and Teachers.International journal of psychology : Journal international de psychologie · 2026
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  19. Network analysis of head and neck symptoms and fear of cancer recurrence in postoperative laryngeal cancer patients: simulation-based intervention modeling.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Article
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678 more citing papers are in PubMed but not listed here.

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 at 1 institution in 1 country.

Donald J RobinaughDepartment of Psychiatry.ORCID 0000-0002-5579-7518
Alexander J MillnerDepartment of Psychology, Harvard University.
Richard J McNallyDepartment of Psychology, Harvard University.
Harvard University · US

Funding

QUESTIONS ABOUT BEREAVEMENT: ANSWERS FROM THE CLOC STUDYR01AG015948 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NESSE, RANDOLPH M · 2000 to 2001
$681k
PRODUCTIVITY, STRESS AND HEALTH IN MIDDLE AND LATE LIFEP01AG005561 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HOUSE, JAMES S · 1985 to 1992
–
NIA NIH HHS AG15948-01NIA NIH HHS AG610757-01NIA NIH HHS P01 AG005561
6 · The paper itself

Abstract

The network approach to psychopathology conceptualizes mental disorders as networks of mutually reinforcing nodes (i.e., symptoms). Researchers adopting this approach have suggested that network topology can be used to identify influential nodes, with nodes central to the network having the greatest influence on the development and maintenance of the disorder. However, because commonly used centrality indices do not distinguish between positive and negative edges, they may not adequately assess the nature and strength of a node's influence within the network. To address this limitation, we developed 2 indices of a node's expected influence (EI) that account for the presence of negative edges. To evaluate centrality and EI indices, we simulated single-node interventions on randomly generated networks. In networks with exclusively positive edges, centrality and EI were both strongly associated with observed node influence. In networks with negative edges, EI was more strongly associated with observed influence than was centrality. We then used data from a longitudinal study of bereavement to examine the association between (a) a node's centrality and EI in the complicated grief (CG) network and (b) the strength of association between change in that node and change in the remainder of the CG network from 6- to 18-months postloss. Centrality and EI were both correlated with the strength of the association between node change and network change. Together, these findings suggest high-EI nodes, such as emotional pain and feelings of emptiness, may be especially important to the etiology and treatment of CG. (PsycINFO Database Record

Indexed as

GriefModels, PsychologicalAgedFemaleHumansLongitudinal StudiesMaleMental Disorders

Identifiers

PMID27505622
PMCPMC5060093
OpenAlexW2429047191

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.