Evidence map›Paper›PMID 36217552›Full record

ArticleJournal of happiness studies2022

Affective Profiles and Psychosocial Adjustment among Chinese Adolescents and Adults with Adverse Childhood Experiences: A Person-Centered Approach.

Qinglu Wu, Junfeng Zhao, Guoxiang Zhao, Xiaoming Li, Hongfei Du, Peilian Chi

Abstract read
In one paragraph

Article in Journal of happiness studies, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

6 authors.

Qinglu WuInstitute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, Zhuhai, China.
Junfeng ZhaoInstitute of Psychology and Behavior, Henan University, Kaifeng, Henan China.
Guoxiang ZhaoDepartment of Psychology, Henan Normal University, Xinxiang, Henan China.
Xiaoming LiDepartment of Health Promotion, Education, and Behavior, University of South Carolina, Columbia, SC US.
Hongfei DuInstitute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, Zhuhai, China.
Peilian ChiDepartment of Psychology, Faculty of Social Sciences, University of Macau, Macau, China.ORCID 0000-0002-1839-1113

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Two studies were conducted to explore the patterns of positive affect (PA) and negative affect (NA) while considering collectivist cultural specificity (dialecticism) and to examine the associations of affective profiles with psychosocial adjustment. Methods: We used two Chinese samples, one comprising adults with adverse childhood experiences ( Results: Three profiles were identified through latent profile analysis: well-adjusted (high PA, low NA), low affective (low PA, low NA), and moderate affective (moderate PA, moderate NA). Participants in the well-adjusted profile had the fewest mental health problems (depressive symptoms, anxiety) and scored highest on personal strengths (self-esteem, gratitude, resilience) and life satisfaction. Participants in the low affective profile had fewer mental health problems than those in the moderate affective profile. Conclusion: Individual differences and cultural variations should be considered when exploring affective profiles. Future interventions aimed at promoting affective well-being should accommodate dialecticism and individual differences in the target population. Supplementary Information: The online version contains supplementary material available at 10.1007/s10902-022-00566-7.

Indexed as

affective profilesdialecticismemotion typologyperson-centered approachpsychosocial adjustment

Identifiers

PMID36217552
PMCPMC9533975

What OpenQuestion holds

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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.