Evidence map›Paper›PMID 39640866›Full record

ArticleJournal of affective disorders reports2024

Gender matters for daily depression: Symptom fluctuations and links to self-expression.

Ran Yan, Natasha Chaku, Nestor L Lopez-Duran, Patricia J Deldin, Adriene M Beltz

Abstract read
In one paragraph

Article in Journal of affective disorders reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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  3. Review
  4. Article
  5. 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

5 authors.

Ran YanDepartment of Psychology, University of Michigan, 530 Church Street, Ann Arbor, MI 48109-1043, United States.
Natasha ChakuDepartment of Psychology, University of Michigan, 530 Church Street, Ann Arbor, MI 48109-1043, United States.
Nestor L Lopez-DuranDepartment of Psychology, University of Michigan, 530 Church Street, Ann Arbor, MI 48109-1043, United States.
Patricia J DeldinDepartment of Psychology, University of Michigan, 530 Church Street, Ann Arbor, MI 48109-1043, United States.
Adriene M BeltzDepartment of Psychology, University of Michigan, 530 Church Street, Ann Arbor, MI 48109-1043, United States.

Funding

Michigan Institute for Clinical and Health Research (MICHR)UL1TR002240 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LUMENG, JULIE C, MASHOUR, GEORGE ALEXANDER · 2017 to 2022
$54.9M
NCATS NIH HHS UL1 TR002240
6 · The paper itself

Abstract

Background: Depression is a global health burden, disproportionately affecting women. A lack of contextual, real-life assessments considering the oft-gendered context (e.g., expression) of daily symptom fluctuations may contribute to this disparity. The current study examines: a) gender differences in daily depressive symptoms - and fluctuations; and b) daily links between self-perceived gender expression and depressive symptoms. Methods: Established adults ( Results: Women experienced greater day-to-day symptom fluctuations than men, after accounting for the gender difference in baseline symptoms ( Conclusions: Results highlight the utility of intensive longitudinal approaches for the study of depression and, for some people, the daily psychological relations with gender self-perceptions. Results also emphasize heterogeneity in depression etiology and the need for personalized basic and applied science. Limitations: Future research on individuals with varying gender identities and clinical experiences is needed.

Indexed as

Depressive symptomsEstablished adulthoodFemininityIntensive longitudinal studyMasculinityPerson-specific

Identifiers

PMID39640866
PMCPMC11619048

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.