Evidence map›Paper›PMID 40308812›Full record

ArticleDigital health

Improving access to postpartum healing in Cesarean delivery women via TikTok: A descriptive cross-sectional study.

Taylor Jamali, Talia Thomas, Melanie Lagomichos

Abstract read
In one paragraph

Article in Digital health. 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
  2. Article
  3. 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.

Taylor JamaliDepartment of OBGYN, Anne Burnett Marion School of Medicine at Texas Christian University, Fort Worth, TX, USA.
Talia ThomasDepartment of OBGYN, Anne Burnett Marion School of Medicine at Texas Christian University, Fort Worth, TX, USA.ORCID https://orcid.org/0009-0000-2523-1647
Melanie LagomichosDepartment of OBGYN, Anne Burnett Marion School of Medicine at Texas Christian University, Fort Worth, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To analyze and explore the products, techniques, and procedures shared on TikTok to assist postpartum mothers in healing cesarean section scars and to evaluate the platform's role in educating patients about cesarean scar healing. This descriptive cross-sectional study was motivated by concerns over the variability and potential misinformation in social media content, which could influence postpartum recovery practices. Methods: A preliminary analysis of TikTok content was conducted using specific search terms related to cesarean scar healing. Videos were evaluated for engagement metrics (likes, views, and comments), content themes, and the types of products or techniques recommended. The relevance of the content to postpartum recovery, including physical and emotional aspects, was assessed. Results: Our analysis found that CS healing is a widely discussed topic on TikTok, with videos serving purposes such as education, destigmatization, entertainment, and product promotion. Most content was created by laypersons or influencers, with only a small portion led by healthcare professionals. Conclusion: TikTok provides a supportive platform for postpartum mothers to share experiences and learn about CS healing. However, misinformation and oversimplification on social media emphasize the importance of consulting healthcare professionals before making medical decisions or altering post-operative healing treatment plans.

Indexed as

cesarean sectionPostpartumscar massagesocial mediaTikTokwound healing

Identifiers

PMID40308812
PMCPMC12041683

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.