Overcoming the digital divide? Low education low income parents are equally likely to report benefits associated with online parenting information
Bibliographic record
Abstract
ABSTRACT Seeking consumer health and wellbeing information online is increasingly common. However, little is known about how people are using information targeted to a specific audience, and what happens as a result of this use. We partnered with Naitre & grandir (N&G), a magazine, website and newsletter offering trustworthy parenting information on child growth, development and health/wellbeing. This study was designed to uncover the outcomes of using online parenting information. We used the theory‐driven Information Assessment Method (IAM) to study parental perception regarding outcomes of specific N&G webpages. We asked this research question: Is there a difference between parents with a low level of education and income vs. other parents? Over an 18‐month study period, 1886 participants submitted 2532 IAM ratings that suggested N&G information was valuable in terms of situational relevance (97.2%), positive cognitive impact (99.7%), intention to use (97.9%) and expectation of benefits for child health and well‐being (69.6%). Results suggested parents with a low level of education and income were less likely to expect that N&G information can improve their child's health and wellbeing. However, there were no differences regarding parental worries, problem prevention, problem management, information exchange (e.g., with professionals) and confidence in decision‐making associated with information use. Results suggest trustworthy online parenting information for all persons, including parents with a low literacy level, can contribute to overcome the digital divide. To our knowledge, this is the first study to assess outcomes of emailed parenting information from a parental viewpoint. In future research, we will describe health and wellbeing outcomes illustrated through stories collected from parents.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.017 |
| Open science | 0.001 | 0.001 |
| 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".