“Let me know when I’m needed”: Exploring the gendered nature of digital technology use for health information seeking during the transition to parenting
Bibliographic record
Abstract
This paper presents results of a qualitative descriptive study conducted to understand parents' experiences with digital technologies during their transition to parenting (i.e. the period from pre-conception through postpartum). Individuals in southwest Ontario who had become a new parent within the previous 24 months were recruited to participate in a focus group or individual interview. Participants were asked to describe the type of technologies they/their partner used during their transition to parenthood, and how such technologies were used to support their own and their family's health. Focus group and interview transcripts were then subjected to thematic analysis using inductive coding. Ten focus groups and three individual interviews were conducted with 26 heterosexual female participants. Participants primarily used digital technologies to: (1) seek health information for a variety of reproductive health issues, and (2) establish social and emotional connections. The nature of such health information work was markedly gendered and was categorized by 2 dominant themes. First, "'Let me know when I'm needed'", characterizes fathers' apparent avoidance of health information seeking and resultant creation of mothers as lay information mediaries. Second, "Information Curation", captures participants' belief that gender biases built-in to popular parenting apps and resources reified the gendered nature of health and health information work during the transition to parenting. Overall, findings indicate that digital technology tailored to new and expecting parents actively reinforced gender norms regarding health information seeking, which creates undue burden on new mothers to become the sole health information seeker and interpreter for their family.
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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 | 0.007 | 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 | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".