Spatial–temporal analysis of suicide clusters for suicide prevention in Hong Kong: a territory-wide study using 2014–2018 Hong Kong Coroner's Court reports
Bibliographic record
Abstract
Background: This study aimed to (i) identify high-risk suicide-methods clusters, based on location of residence and suicide incidence; and (ii) compare the characteristics of cases and spatial units inside and outside clusters. Methods: Suicide data of 4672 cases was obtained from the Coroner's Court reports in Hong Kong (2014-2018). Monthly aggregated suicide numbers based on location of residence, and suicide incidence, were obtained in small tertiary planning units (STPUs). Community-level characteristics and population of STPUs were retrieved from 2016 Census. Retrospective space-time analyses were performed to identify locations with elevated suicide rates over specific time periods, i.e., spatial-temporal clusters. Clusters were evaluated for overall suicide (any method), as well as jumping, hanging, and charcoal burning methods, in location of residence and suicide incidence. Bi-variate analysis was performed to compare the characteristics of cases, and spatial units, inside and outside the clusters. Findings: Suicide clusters involving jumping and charcoal burning were identified, but no hanging clusters were found. The within-cluster distribution of types of housing was different from that of outside. For most of the overall suicide and suicide by jumping clusters, spatial units within the clusters were more socially disadvantaged compared to those outside. Interpretation: Clusters varied by suicide methods, location of residence and location of incidence. The findings highlighted the need for consistent and concerted support from different stakeholders within suicide clusters, to ensure appropriate design, implementation and sustainability of effective suicide prevention programs. Funding: General Research Fund (37000320) and seed fund from the University of Hong Kong (104006710).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".