PERCEIVED ACCURACY OF INFORMATION SOURCES CONSULTED BY FAMILIES WHOSE CHILDREN HAVE EPILEPSY
Bibliographic record
Abstract
Objectives: To assess which information sources are accessed by families whose children have epilepsy and the perceived accuracy of these sources. Methods: A structured interview of 84 families of children with epilepsy followed through the Neurology or Refractory Epilepsy clinics of a tertiary care children's hospital was conducted to assess epilepsy-specific information sources accessed and perceived accuracy of these sources. Results: Families accessed a mean of 3.5 sources within/specifically recommended by the clinic or family doctor and 4.1 sources outside of these areas. Families of children with intractable epilepsy, higher educated parents, but not those of higher socioeconomic status consulted more extensively. Perceived accuracy of information rated highest for clinic-recommended Internet sites (100%), clinic nurse (97%) and neurologist (93%). Sources external to clinic had variable ratings; those with greatest perceived accuracy included other Internet sites or family members within the medical profession (85% for both) and lay organizations (84%). Friends within the medical profession, other families and complimentary health providers also ranked highly. Conclusion: Families whose children have epilepsy perceive the clinic nurse as a readily accessible and accurate source of information. Neurology-clinic recommended internet sites and books were more helpful to families than general handouts.
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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 | 0.007 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".