ArticleJournal of medical Internet research2021
A Determinants-of-Fertility Ontology for Detecting Future Signals of Fertility Issues From Social Media Data: Development of an Ontology.
Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Screens and society: how media narratives influence fertility and reshape childbearing choices; a systematic review.BMC women's health · 2025Pooled it
- An Ontology for the Adoption of Medical Devices in Health Care Organizations: Design and Development Study.Journal of medical Internet research · 2026Article
- Toward clearer recognition and easier usefulness: development of a cross-lingual atherosclerotic cerebrovascular disease ontology.Database : the journal of biological databases and curation · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSouth Korea has the lowest fertility rate in the world despite considerable governmental efforts to boost it. Increasing the fertility rate and achieving the desired outcomes of any implemented policies requires reliable data on the ongoing trends in fertility and preparations for the future based on these trends.
objectiveThe aims of this study were to (1) develop a determinants-of-fertility ontology with terminology for collecting and analyzing social media data; (2) determine the description logics, content coverage, and structural and representational layers of the ontology; and (3) use the ontology to detect future signals of fertility issues.
methodsAn ontology was developed using the Ontology Development 101 methodology. The domain and scope of the ontology were defined by compiling a list of competency questions. The terms were collected from Korean government reports, Korea's Basic Plan for Low Fertility and Aging Society, a national survey about marriage and childbirth, and social media postings on fertility issues. The classes and their hierarchy were defined using a top-down approach based on an ecological model. The internal structure of classes was defined using the entity-attribute-value model. The description logics of the ontology were evaluated using Protégé (version 5.5.0), and the content coverage was evaluated by comparing concepts extracted from social media posts with the list of ontology classes. The structural and representational layers of the ontology were evaluated by experts. Social media data were collected from 183 online channels between January 1, 2011, and June 30, 2015. To detect future signals of fertility issues, 2 classes of the ontology, the socioeconomic and cultural environment, and public policy, were identified as keywords. A keyword issue map was constructed, and the defined keywords were mapped to identify future signals. R software (version 3.5.2) was used to mine for future signals.
resultsA determinants-of-fertility ontology comprised 236 classes and terminology comprised 1464 synonyms of the 236 classes. Concept classes in the ontology were found to be coherently and consistently defined. The ontology included more than 90% of the concepts that appeared in social media posts on fertility policies. Average scores for all of the criteria for structural and representations layers exceeded 4 on a 5-point scale. Violence and abuse (socioeconomic and cultural factor) and flexible working arrangement (fertility policy) were weak signals, suggesting that they could increase rapidly in the future.
conclusionsThe determinants-of-fertility ontology developed in this study can be used as a framework for collecting and analyzing social media data on fertility issues and detecting future signals of fertility issues. The future signals identified in this study will be useful for policy makers who are developing policy responses to low fertility.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.