Evidence map›Paper›PMID 37867796›Full record

ArticleHeliyon2023

Maternal postpartum depression literacy subtypes: A latent profile analysis.

Kangfen Li, Jie Lu, Yan Pang, Xinlie Zheng, Ran Liu, Min Ren, Suhua Tu

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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  4. Review
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

7 authors.

Kangfen LiSchool of Nursing, Southwest Medical University, Luzhou, Sichuan, China.
Jie LuSchool of Nursing, Southwest Medical University, Luzhou, Sichuan, China.
Yan PangDepartment of Nursing, Chengdu Women and Children's Central Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Xinlie ZhengSchool of Nursing, Southwest Medical University, Luzhou, Sichuan, China.
Ran LiuSchool of Nursing, Southwest Medical University, Luzhou, Sichuan, China.
Min RenDepartment of Obstetrics, Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Suhua TuNursing Department, Afliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: To explore the potential categories and characteristic differences of maternal postpartum depression literacy. Design: Cross-sectional survey. Methods: From February 2023 to April 2023, convenience sampling was used to survey 278 women attending postnatal visits to three tertiary level A hospitals. The study included general demographic characteristics, postpartum depression literacy scale, and family caring index scale. Latent profile analysis was performed to identify the categories of maternal postpartum depression literacy, and multiple disordered logistic regression was used to analyze the influencing factors of different categories. Results: Maternal postpartum depression literacy was divided into three categories: low literacy (41.0 %), moderate literacy (32.4 %), and high literacy (26.6 %). The results showed that work status, education level, whether the pregnancy was planned, whether or not they had participated in mental health-related courses, and family functioning status were factors influencing the category of maternal postpartum depression literacy ( Conclusion: There was heterogeneity in postpartum depression literacy among mothers. Medical staff should implement targeted interventions according to potential category characteristics and influencing factors to improve the level of postpartum depression literacy.

Indexed as

Latent profile analysisMaternalMental health literacyPostpartum depression

Identifiers

PMID37867796
PMCPMC10585387

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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.