How Do People Experiencing Schizophrenia Spectrum Disorders or Other Psychotic Disorders Use the Internet to Get Information on Their Mental Health? Literature Review and Recommendations
Bibliographic record
Abstract
BACKGROUND: Studies show that the Internet has become an influential source of information for people experiencing serious psychiatric conditions such as schizophrenia spectrum disorders or other psychotic disorders, among which the rate of Internet users is growing, with rates ranging from 33.3% to 79.5% given the country. Between 20.5% and 56.4% of these Internet users seek mental health information. OBJECTIVE: Focusing on this population's Web searches about their mental health, this paper examines what type of content they look for and what could be the benefits and disadvantages of this navigation. METHODS: We conducted a literature review through medical and psychological databases between 2000 and 2015 using the keywords "Internet," "Web," "virtual," "health information," "schizophrenia," "psychosis," "e-mental health," "e-support," and "telepsychiatry." RESULTS: People experiencing schizophrenia spectrum disorders or other psychotic disorders wish to find on the Internet trustful, nonstigmatizing information about their disease, flexibility, security standards, and positive peer-to-peer exchanges. E-mental health also appears to be desired by a substantial proportion of them. In this field, the current developments towards intervention and early prevention in the areas of depression and bipolar and anxiety disorders become more and more operational for schizophrenia spectrum disorders and other psychotic disorders as well. The many benefits of the Internet as a source of information and support, such as empowerment, enhancement of self-esteem, relief from peer information, better social interactions, and more available care, seem to outbalance the difficulties. CONCLUSIONS: In this paper, after discussing the challenges related to the various aspects of the emergence of the Internet into the life of people experiencing schizophrenia spectrum disorders or other psychotic disorders, we will suggest areas of future research and practical recommendations for this major transition.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".