Parental Information-Use Strategies in a Digital Parenting Environment and Their Associations With Parental Social Support and Self-Efficacy: Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: In today's digital society, the acquisition of parenting information through online platforms such as social networking sites (SNSs) has become widespread. Amid the mix of online and offline information sources, there is a need to discover effective information-seeking methods for solving parenting problems. OBJECTIVE: This study aimed to identify patterns of information use among parents of young children in the digital age and elucidate the characteristics of these patterns through a comparative analysis of parental social support and self-efficacy. METHODS: An internet-based survey was administered to fathers and mothers of children aged 0-3 years. Convenience sampling, facilitated by an internet-based survey company, was adopted, and data from 227 fathers and 206 mothers were analyzed. The survey included questions on personal characteristics, frequency of use of different sources of parenting information (websites, SNSs, parenting apps, family, friends, and professionals), availability of parental social support, and parental self-efficacy. The Partitioning Around Medoids (PAM) clustering algorithm was used to identify patterns in parenting information use. RESULTS: A total of 4 clusters were identified: multisource gatherers (n=161), offline-centric gatherers (n=105), online-centric gatherers (n=86), and minimal information gatherers (n=68). The availability of parental social support was perceived to be relatively higher among multisource and offline-centric gatherers compared with online-centric and minimal information gatherers. Parental self-efficacy was highest among multisource gatherers, followed by offline-centric and online-centric gatherers, and lowest among minimal information gatherers. CONCLUSIONS: This study contributes to the evidence that online information can effectively complement offline information in addressing parenting challenges, although its ability to fully replace offline sources remains limited. Parenting support professionals are encouraged to understand parents' current information use strategies and actively foster their social relationships, helping them to adopt more diverse and comprehensive approaches to information use.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".