Learning to parent from Google? Evaluation of available online health evidence for parents of preterm infants requiring neonatal intensive care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it