Parenting Information on Social Media: Systematic Literature Review
Bibliographic record
Abstract
BACKGROUND: Social media has become extremely popular among parents to seek parenting information. Despite the increasing academic attention to the topic, studies are scattered across various disciplines. Therefore, this study broadens the scope of the existing reviews by transcending narrow academic subdomains and including all relevant research insights related to parents' information seeking on social media and its consequent effects. OBJECTIVE: The aims of this systematic literature review were to (1) identify influential journals and scholars in the field; (2) examine the thematic evolution of research on parenting and social media; and (3) pinpoint research gaps, providing recommendations for future exploration. METHODS: On the basis of a criteria for identifying scholarly publications, we selected 338 studies for this systematic literature review. We adopted a bibliometric analysis combined with a content thematic analysis to obtain data-driven insights with a profound understanding of the predominant themes in the realm of parenting and social media. RESULTS: The analysis revealed a significant increase in research on parenting and social media since 2015, especially in the medical domain. The studies in our review spanned 232 different research fields, and the most prolific journal was JMIR Pediatrics and Parenting. The thematic analysis identified 4 emerging research themes in the studies: parenting motivations to seek information, nature of parenting content on social media, impact of parenting content, and interventions for parents on social media. CONCLUSIONS: This study provides critical insights into the current research landscape of parenting and social media. The identified themes, research gaps, and future research recommendations provide a foundation for future studies, guiding researchers toward valuable areas for exploration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.031 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".