Bibliographic record
Abstract
OBJECTIVES: The research sought to determine if the health advice provided in online discussion forms aimed at parents of young children is accurate and in agreement with evidence found in evidence-based resources and to discover whether or not these forums are an avenue for misinformation. METHODS: To determine which online forums to use, Google was searched using five common childhood ailments. Forums that appeared five or more times in the first five pages of the Google search for each question were considered. Of these forums, those that met the inclusion criteria were used. Data from a six-month time period was collected and categorized from the discussion forums to analyze the advice being provided about common childhood ailments. Evidence-based resources were used to analyze the accuracy of the advice provided. RESULTS: Two discussion forums were chosen for analysis. Seventy-four questions from one and 131 questions from the other were health related. Data were not analyzed together. Of the health-related questions on the 2 forums, 65.5% and 51.8%, respectively, provided some type of advice. Of the advice provided, 54.1% and 47.2%, respectively, agreed with the evidence provided in evidence-based resources. A further 16.2% and 6.3% was refuted or was somewhat refuted by the evidence found in evidence-based resources. CONCLUSION: While roughly half of the health-related advice provided in online discussion forums aimed at parents of young children is accurate, only a small portion of the advice is incorrect; therefore, these sources are not a major concern for the spread of misinformation.
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 | 0.036 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".