Investigating the Transition to Parenting within a Digital Health Context
Bibliographic record
Abstract
Abstract Background The ubiquity of digital technology use created the need to understand parents’ digital technology use in pre-conception, pregnancy and the postpartum period (the transition to parenting). This research investigated the role of digital technology in the lives of individuals in the transition to parenting. Methods A qualitative descriptive study used purposive sampling. Participants were eligible if they were new parents, up to 2 years postpartum, were 16-35 years old, and English speaking. Focus groups were recorded and transcribed; thematic data analysis was used. Results 26 participants identified as heterosexual females reported prolific use of digital technologies to direct fertility (e.g., ovulation timing), for information seeking regarding foetal growth and development, to prepare for labour and delivery, and in searching for a sense of community during postpartum. Smartphone technologies, social media, and prenatal apps were predominantly used. Participants expressed concerns related to their personal patterns of use, and the potential negative impacts. Themes included: “Is this normal; is this happening to you?!”, “Am I having a heart attack; what is this?”, “Anyone can put anything on Wikipedia”: Managing the Negative Impacts of Digital Information. Conclusions Digitally informed parenting practices include enhanced access to information regarding normal infant growth and development. Immediacy of information access was valued over integrity. Important implications of this work relate to the need for effective digital health literacy skills. Future research on the gendered use of digital technologies among parents is needed. Key messages • Digital technology use creates important health implications for pre and post-natal care among parents. • Digital technology use is requisite for prenatal public health education.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".