Bibliographic record
Abstract
Background: Hikikomori, a severe and often prolonged social withdrawal observed primarily in young people, was first described in Japan, but cases have now been reported in many other countries. Methods: A review paper on hikikomori has been prepared following the literature searches in 3 databases. Search terms related to hikikomori included epidemiology, globalization, diagnosis, treatment, comorbidity, and COVID-19. Conclusions: Hikikomori was first reported in Japan and has been described in detail by researchers there, but there are now reports in many countries of hikikomori-like cases. It occurs primarily in young people, often men in their late teens and early twenties who isolate themselves, sometimes confining themselves to their homes for months or even years. It has been proposed that hikikomori has increased in recent years in part because of advances in information technology that result in decreased socialization. Hikikomori was originally considered a non-psychotic phenomenon, but comorbidity with psychiatric disorders is often present and should be considered during diagnosis. Considerable efforts have been made in recent years to establish reliable, widely applicable guidelines for the diagnosis and treatment of hikikomori. There is very little information with regard to neurobiology, although involvement of the immune system, oxidative stress, and the social brain network has been proposed. It is widely agreed that hikikomori must be treated in a multi-dimensional fashion, with family support very important. Lessons learned from these treatment approaches are relevant to the potential increased risk of social withdrawal arising from COVID-19 pandemic lockdowns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".