O que a internet ensina à paciente obstétrica sobre a analgesia de parto?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: It has been observed a general public increased search on the Internet for health information, including Anesthesiology. The objective of this study was to evaluate the information available to the lay person in Portuguese on the internet about labor analgesia for the Brazilian population. METHOD: Using the term "labor anesthesia", the first 20 sites found on Google in November 2014 were evaluated by two resident physicians and classified as medical and non-medical. Legibility and Design - accessibility, reliability and navigability-were compared using Flesch Reading Ease Score (FRESH) and Minervation validation tool for healthcare websites (LIDA) tools. The websites' content was confronted with that of the medical literature. RESULTS: Medical and non-medical websites were considered difficult to read according to FRESH. Regarding the design, there was no difference between groups regarding navigability, however, accessibility was considered superior in non-medical websites (p = 0.042); while reliability was higher in medical websites (p = 0.019). CONCLUSIONS: With the increased search for health information on the Internet and concern about improving the quality of childbirth care, it is fundamental that the content available to the layperson about labor analgesia is of quality and well understood. This study demonstrated that both medical and non-medical websites are difficult to read and that non-medical websites are more accessible while the medical ones are more accurate.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".