Understanding the Tensions of “Good Motherhood” Through Women’s Digital Technology Use: Descriptive Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Research suggests that expectant and new mothers consult and value information gathered from digital technologies, such as pregnancy-specific mobile apps and social media platforms, to support their transition to parenting. Notably, this transitional context can be rich with profound physiological, psychological, and emotional fluctuation for women as they cope with the demands of new parenting and navigate the cultural expectations of "good motherhood." Given the ways in which digital technologies can both support and hinder women's perceptions of their parenting abilities, understanding expectant and new mothers' experiences using digital technologies and the tensions that may arise from such use during the transition to parenting period warrants nuanced exploration. OBJECTIVE: This study aims to understand mothers' use of digital technologies during the transition to parenting period. METHODS: A descriptive qualitative study was conducted in a predominantly urban region of Southwestern Ontario, Canada. Purposive and snowball sampling strategies were implemented to recruit participants who had become a parent within the previous 24 months. Researchers conducted focus groups using a semistructured interview guide with 26 women. The interviews were audio recorded, transcribed, and thematically analyzed. RESULTS: Participants' experiences of using digital technologies in the transition to parenting period were captured within the overarching theme "balancing the tensions of digital technology use in the transition to parenting" and 4 subthemes: self-comparison on social media, second-guessing parenting practices, communities of support, and trusting intuition over technology. Although digital technologies purportedly offered "in-the-moment" access to community support and health information, this came at a cost to mothers, as they described feelings of guilt, shame, and self-doubt that provoked them to question and hold in contention whether they were a good mother and using technology in a morally upright manner. CONCLUSIONS: These findings raise critical questions concerning the promotion and commercialization of digital technologies and the ways in which they can further push the boundaries of hegemonic parenting practices, provoke feelings of inadequacy, and compromise well-being among expectant and new mothers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".