The Quality of Internet Websites for People Experiencing Psychosis: Pilot Expert Assessment
Bibliographic record
Abstract
BACKGROUND: Clinicians need to be able to assess the quality of the available information to aid clinical decision-making. The internet has become an important source of health information for consumers and their families. OBJECTIVE: This study aimed to rate the quality of websites with psychosis-related information (to provide clinicians with a basis for recommending material to guide clinical decision-making with consumers and their families), using a validated instrument as well as a purpose-developed checklist, and consider improvement in quality over a 4-year period. METHODS: Two measures of website quality were used: the DISCERN scale and the Psychosis Website Quality Checklist (PWQC). Terms related to psychosis, including "psychotic," "psychosis," "schizophrenia," "delusion," and "hallucination," were entered into Google, and the first 25 results were analyzed. In total, 6 raters with varying health professional backgrounds were used to evaluate the websites across two time points: January-March 2014 and January-March 2018. RESULTS: Of the 25 websites rated, only the 6 highest ranked websites achieved a DISCERN score, indicating that they were of "good" quality (51-62 out of a possible 75), while the mean score of the websites (mean 43.96, SD 12.08) indicated an overall "fair" quality. The PWQC revealed that websites scored highly on "availability and usability" (mean 16.82, SD 3.96) but poorly on "credibility" (mean 20.99, SD 6.68), "currency" (mean 5.16, SD 2.62), and "breadth and accuracy" (mean 77.87, SD 23.20). Most sites lacked information about early intervention, recreational drug use and suicide risk, with little change in content over time. Stating an editorial or review process on the website (found in 56% of websites) was significantly associated with a higher quality score on both scales (the DISCERN scale, P=.002; the PWQC, P=.006). CONCLUSIONS: The information on the internet available for clinicians to recommend to people affected by psychosis tended to be of "fair" quality. While higher-quality websites exist, it is generally not easy way to assess this on face value. Evidence of an editorial or review process was one indicator of website quality. While sites generally provided basic clinical information, most lacked material addressing weighing up risks and benefits of medication and alternatives, the role of coercive treatment and other more contentious issues. Insufficient emphasis is placed on detailed information on early intervention and importance of lifestyle modifications or how families and friends can contribute. These are likely to be the very answers that consumers and carers are seeking and this gap contributes to unmet needs among this group. We suggest that clinicians should be aware of what is available and where there are gaps.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".