Learning to parent from Google? Evaluation of available online health evidence for parents of preterm infants requiring neonatal intensive care
Bibliographic record
Abstract
The study aim was to identify and evaluate the reliability and quality of online resources for parents of preterm infants seeking health information about their infant using the DISCERN tool and Health on Net code. An Internet search ( www.google.com ) was used to identify websites for parents of preterm infants on their infants' health and health issues. For each search, the top 100 "hits" were downloaded, yielding 1200 websites. After reviewing websites for exclusion criteria and duplicates, 197 websites remained and were analyzed. According to the DISCERN tool, the websites had a moderate reliability score (mean = 29.88, standard deviation = 4.88, range: 18-40), moderate treatment score (mean = 24.15, standard deviation = 5.79, range: 10-35), and moderate overall quality score (mean = 3.41, standard deviation = 0.89, range: 1-5). Only 24 (12.2%) websites had current Health on Net code approval and no other websites met full eligibility for certification. Overall, the reliability and quality of information available online to parents of preterm infants is lacking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.253 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".