Investigation of Digital Technology Use in the Transition to Parenting: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The transition to parenting-that is, the journey from preconception through pregnancy and postpartum periods-is one of the most emotionally charged and information-intense times for individuals and families. While there is a developing body of literature on the use and impact of digital technology on the information behaviors of children, adolescents, and young adults, personal use of digital technology during the transition to parenting and in support of infants to 2 years of age is relatively understudied. OBJECTIVE: The purpose of this study was to enhance our understanding of the ways digital technologies contribute to the experience of the transition to parenting, particularly the role these technologies play in organizing and structuring emerging pregnancy and early parenting practices. METHODS: A qualitative descriptive study was conducted to understand new parents' experiences with and uses of digital technology during 4 stages-prenatal, pregnancy, labor, and postpartum-of their transition to becoming a new parent. A purposive sampling strategy was implemented using snowball sampling techniques to recruit participants who had become a parent within the previous 24 months. Focus groups and follow-up interviews were conducted using semistructured interview guides that inquired about parents' type and use of technologies for self and family health. Transcribed audio recordings were thematically analyzed. RESULTS: A total of 10 focus groups and 3 individual interviews were completed with 26 participants. While recruitment efforts targeted parents of all genders and sexual orientations, all participants identified as heterosexual women. Participants reported prolific use of digital technologies to direct fertility (eg, ovulation timing), for information seeking regarding development of their fetus, to prepare for labor and delivery, and in searching for a sense of community during postpartum. Participants expressed their need for these technologies to assist them in the day-to-day demands of preparing for and undertaking parenting, yet expressed concerns about their personal patterns of use and the potential negative impacts of their use. The 3 themes generated from the data included: "Is this normal; is this happening to you?!", "Am I having a heart attack; what is this?", and "Anyone can put anything on Wikipedia": Managing the Negative Impacts of Digital Information. CONCLUSIONS: Digital technologies were used by mothers to track menstrual cycles during preconception; monitor, document, and announce a pregnancy during the prenatal stage; prepare for delivery during labor/birth stage; and to help babies sleep, document/announce their birth, and connect to parenting resources during the postpartum stage. Mothers used digital technologies to reassure themselves that their experiences were normal or to seek help when they were abnormal. Digital technologies provided mothers with convenient means to access health information from a range of sources, yet mothers were apprehensive about the credibility and trustworthiness of the information they retrieved. Further research should seek to understand how men and fathers use digital technologies during their transition to parenting. Additionally, further research should critically examine how constant access to information affects mothers' perceived need to self-monitor and further understand the unintended health consequences of constant surveillance on new parents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 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".