Fathers and Mothers with Low Socioeconomic Status Anticipate More Benefits from Trustworthy Easy-to- Read Online Child-Related Information Compared to Other Parents: The 4-Year IAM Prospective Time Series
Bibliographic record
Abstract
Almost all parents seek online child-related information. This study focuses on parents’ experience of using information from an easy-to-read parenting website, Naître et Grandir (N&G), specifically parents with low socioeconomic status (SES). SES is correlated with health literacy, a major determinant of child education and health. In January 2019, the Information Assessment Method (IAM) questionnaire was improved and implemented in a smartphone application (IAM + N&Gsmart) to reach more low SES parents. We measured the influence of IAM + N&Gsmart on the frequency with which low SES parents responded to the IAM survey of N&G webpages and the relative proportions of anticipated benefits of the N&G content. We also compared these benefits among fathers and mothers. This was a 4-year prospective time series. For each N&G webpage, parents were invited to complete an IAM questionnaire and report anticipated outcomes. IAM data were collected before (2017–2018) and after (2019–2020) the intervention (IAM + N&Gsmart launch) from Quebec parents of 0–8-year-old children who completed at least one IAM questionnaire. Descriptive and inferential statistical analyses were applied. Participants completed 10,362 IAM questionnaires. Low SES participants anticipated more benefits than other participants, and particularly low SES fathers more than low SES mothers. The proportion of responses and reported benefits from low SES participants increased post-intervention. Results suggest that increasing literacy-oriented web content can lead to greater benefits among low SES parents, and that increasing father awareness and father-inclusive content can lead to even greater benefits among low SES fathers. Parent socioeconomic status (SES) and health literacy are linked and together constitute a major determinant of child education and health. Prior studies suggest that low SES mothers can benefit the most from trustworthy easy-to-read information. Results suggest that low SES parents can benefit the most from trustworthy easy-to-read online child-related information (compared to other parents) especially low SES fathers (compared to low SES mothers). Simple evaluation questions located on each web page can help low SES parents provide valuable feedback to web editors for assessing and improving the information.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".