Investigating the gendered nature of new parents’ digital technology use for health information
Bibliographic record
Abstract
Abstract Background Parenting is marked by intense emotional and health information needs for individuals and families. Understanding how digital technologies are used by new parents may allow public health organizations to tailor digital health information resources and delivery methods to better meet needs. Methods A qualitative descriptive study was conducted to understand new parents’ experiences with digital technology during their transition to parenting. Individuals in Ontario, Canada who had become a parent in the past 24 months were recruited to participate in a focus group. Participants were asked to describe the technologies used to support their parenting and how they were used to support self and family health. Focus group data were subjected to thematic analysis using inductive coding. Results Focus groups were conducted with 26 heterosexual female participants. Participants primarily used digital technologies (smartphone, social media) to seek information on maternal, foetal, and infant health and establish social and emotional supports. Parental health information work was gendered and categorized by 2 dominant themes. First, “‘Let me know when I'm needed'” typifies fathers’ limited health information seeking and reinforces mothers as lay information mediaries. Second, “Information Curation” captures participants’ belief that gender biases built-in to digital parenting resources reified the gendered nature of health information work for new parents. Conclusions While parents regularly use digital resources for health information seeking, the gendered nature of such digital resources reinforces gendered divisions of health work. Key messages • Digital technologies tailored to new parents actively reinforces gender norms. • Public health organizations have an opportunity to engage with new parents to identify how digital information resources can be created that support equitable division of health information work.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".