Use of Social Network Facebook for Mental Health Prevention and Counselling
Bibliographic record
Abstract
Introduction Modern communication facilitates telepsychiatry in native language everywhere in the world. Using social networks (facebook), many pages promote mental health and provide advices for mental health problems. Unfortunately, many were inappropriate, so we decided to give confidential cost-free advice to those who asked for counselling. Aims Present one-year experience in counselling using facebook page administered by a psychiatrist in Serbian/Croatian/Bosnian language. Aims Present number of page likes, counselled people and most frequent problems. Methods Review page statistics and inbox. Results The facebook page provides mental health educational content, confidential counselling with a psychiatrist, sharing opinion with other members and group discussion. There are 10.573 members (88% women, 12 % men). Up to 50.000 people are reached weekly. Majority of the members is age 18 to 44 (77%), coming from: ex Yugoslavia countries 8008 (75,7%), EU 1912 (18,1%), Switzerland 1,1%, USA, Canada, Australia 1,2% . There were 95 persons looking for help via inbox. The most frequent problems were: depression 24,2%, marriage/relationship problems 24,2%, emotional reactions after flooding in Bosnia 13,7%, anxiety disorders13,6%, low self-confidence 6,3%, emotional or physical abuse 6,3%, suicidal 6,3%, psychosis 3,1%, OCD 2,1% and 25,2% asked how to help others. Tele-psychiatric service with GP commenced during the flooding. Supervision has been provided by a psychiatrist. There were 52 reviews with the rates: excellent 92,3%, very good 7,7. Conclusions Social networks are useful for mental health promotion, education and counselling people around the world. Problems are acute psychiatric conditions and legal issues (suicidal and abused persons).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.005 |
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".