An evaluation of the quality of online perinatal depression information
Bibliographic record
Abstract
BACKGROUND: During the perinatal period (including pregnancy and up to 12 months after childbirth), expectant and new mothers are at an elevated risk of developing depression. Inadequate knowledge about perinatal depression and treatment options may contribute to the low help-seeking rates exhibited by perinatal people. The Internet can be an accessible source of information about perinatal depression; however, the quality of this information remains to be evaluated. The purpose of this study was to assess the quality of perinatal depression information websites. METHODS: After review, 37 websites were included in our sample. To assess overall website quality, we rated websites based on their reading level (Simple Measure of Gobbledegook; SMOG), information quality (DISCERN), usability (Patient Education Materials Assessment Tool; PEMAT), and visual design (Visual Aesthetics of Website Inventory; VisAWI). RESULTS: Websites often exceeded the National Institute of Health's recommended reading level of grades 6-8, with scores ranging from 6.8 to 13.5. Website information quality ratings ranged from 1.8 to 4.3 out of 5, with websites often containing insufficient information about treatment choices. Website usability ratings were negatively impacted by the lack of information summaries, visual aids, and tangible tools. Visual design ratings ranged from 3.2 to 6.6 out of 7, with a need for more creative design elements to enhance user engagement. CONCLUSIONS: This study outlines the characteristics of high-quality perinatal depression information websites. Our findings illustrate that perinatal depression websites are not meeting the needs of users in terms of reading level, information quality, usability, and visual design. Our results may be helpful in guiding healthcare providers to reliable, evidence-based online resources for their perinatal patients.
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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 | 0.019 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".