Unmet Health Care Use Among Socially Withdrawn Youth (Hikikomori) in South Korea: Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: Hikikomori, a condition of severe social withdrawal, is a global public health issue characterized by prolonged isolation. Despite its growing prevalence, little is known about the health care needs and use patterns of socially withdrawn youth. Objective: The study aimed to examine the association between hikikomori status and unmet health care use to inform targeted interventions. Methods: Data were obtained from the 2022 Korean Youth Living Conditions Survey, a nationally representative cross-sectional survey of 14,966 participants aged 19 to 34 years. Survey weights were applied to account for the sampling design. Hikikomori status was classified based on self-reported withdrawal behaviors, stratified by severity and duration. Unmet health care use in physical and mental health was assessed. Adjusted prevalence ratios (aPRs) with 95% CIs were estimated using generalized estimating equation models, and logistic regression was applied for subgroup analyses. Results: The weighted prevalence of perceived need for mental health services was 63.9% in the hikikomori group versus 50.5% in the non-hikikomori group (aPR 1.27, 95% CI 1.15-1.41). Unmet health care use was higher among individuals with hikikomori for physical care (aPR 3.33, 95% CI 2.16-5.13) and mental health care (aPR 4.46, 95% CI 2.92-6.81). Associations strengthened with greater severity and longer duration: for unmet mental health care use, aPRs were 4.14 (95% CI 2.64-6.49) for stage 1 and 9.52 (95% CI 3.67-24.65) for stage 2; by duration, aPRs were 2.57 (95% CI 1.11-5.96) for pre-hikikomori and 5.44 (95% CI 3.44-8.58) for hikikomori. Effect modification was observed by labor force participation, with higher risks among those not in the labor force (P for interaction <.05). Conclusions: Hikikomori is strongly associated with unmet health care use, particularly in mental health, with risks amplified by severity and duration. Tailored policies, including community-based outreach and remote health care interventions, are urgently needed to address these 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".