Evidence map›Paper›PMID 34713126›Full record

ArticleFrontiers in digital health2021

Analysis of Online Peripartum Depression Communities: Application of Multilabel Text Classification Techniques to Inform Digitally-Mediated Prevention and Management.

Alexandra Zingg, Tavleen Singh, Sahiti Myneni

Abstract read
In one paragraph

Article in Frontiers in digital health, 2021. 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
  2. Article
  3. Article
  4. 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.

Alexandra ZinggSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.
Tavleen SinghSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.
Sahiti MyneniSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.

Funding

Pragmatics to Reveal Intention in Social Media (PRISM) for Health PromotionR01LM012974 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI MYNENI, SAHITI · 2019 to 2022
$1.5M
NLM NIH HHS R01 LM012974
6 · The paper itself

Abstract

Peripartum depression (PPD) is a significant public health problem, yet many women who experience PPD do not receive adequate treatment. In many cases, this is due to social stigmas surrounding PPD that prevent women from disclosing their symptoms to their providers. Examples of these are fear of being labeled a "bad mother," or having misinformed expectations regarding motherhood. Online forums dedicated to PPD can provide a practical setting where women can better manage their mental health in the peripartum period. Data from such forums can be systematically analyzed to understand the technology and information needs of women experiencing PPD. However, deeper insights are needed on how best to translate information derived from online forum data into digital health features. In this study, we aim to adapt a digital health development framework,

Indexed as

digital healthmachine learningmental healthperipartumsocial media

Identifiers

PMID34713126
PMCPMC8521806

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.