An Investigation of Mhealth and Digital Health Literacy Among New Parents During COVID-19
Bibliographic record
Abstract
INTRODUCTION: Especially during the COVID-19 pandemic, parents were expected to understand increasingly sophisticated information about health issues and healthcare systems and access online resources as a part of their caregiving role. Yet little is known about parents' online digital technology use and digital health literacy skill. This study aimed to investigate parents' digital technology use, their self-reported digital health literacy skill, and demographic information as potential factors influencing their use of digital technologies. METHODS: An online survey utilizing convenience sampling was administered to new parents during the COVID-19 pandemic that inquired about their demographic information, digital technology use, and digital health literacy skills within Ontario, Canada. RESULTS: A total of 151 individuals responded to the survey; these were primarily mothers (80%) who self-reported as white (72%), well-educated 86%), heterosexual (86%) females (85%) with incomes over $100,00 per year (48%). Participants reported consistent and persistent online activity related to their parenting role and mostly via mobile smartphone devices (92%). Participants had moderate to high digital health literacy skills, greater than the Canadian national average. Almost half of participants reported negative health and well-being consequences from their digital online behaviours. There were no significant relationships between technology use, digital health literacy skill, and demographic variables. DISCUSSION: The COVID-19 pandemic has reinforced the need for and importance of effective and equitable digital health services. Important opportunities exist within clinical practice and among parenting groups to proactively address the physical and mental health implications of digital parenting practices. Equally important are opportunities to insert into clinical workflow the inquiry into parents' online information-seeking behaviours, and to include digital health literacy as part of prenatal/postnatal health education initiatives.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".