Perceived outcomes of online parenting information according to self‐selected participants from a population of website users
Bibliographic record
Abstract
ABSTRACT Looking for consumer health/well‐being 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/well‐being. This study was designed to uncover the outcomes of online parenting information. We used the theory‐driven Information Assessment Method (IAM) to study parental perceptions regarding outcomes of specific N&G web pages. A research question was: Is there a difference between parents with a low level of education and income vs. other parents? Over an 8‐month study period, 4007 participants submitted 4862 IAM ratings that suggested N&G information was valuable in terms of situational relevance (93.7%), positive cognitive impact (92.9%), intention to use (85.7%), and expectation for child health/well‐being benefit (82.4%). In addition, results suggested participants with a low level of education and income were more likely to (i) seek and use information for the child of someone else, and (ii) expect being more engaged in decision‐making for their child, and being less worried regarding a problem concerning their child. Our results do not support an association between the combined level of income and education, and perceived outcomes of information. This is the first study to assess outcomes of emailed parenting information from a parental viewpoint. More research is needed to better understand outcomes of targeted online information, which may ultimately contribute to improve people's health/well‐being.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".