Association between Mental Health Apprehensions by Police and Monthly Income Assistance (Welfare) Payments
Bibliographic record
Abstract
OBJECTIVE: Social misconduct, increased police activity, and increased emergency department (ED) use are associated with monthly income assistance (welfare) payments. The relation, if any, between welfare payments and mental health and addictions presentations to the ED requiring police involvement remains unknown. Our purpose was to determine if a relation exists between mental health apprehensions (MHAs) by police and monthly welfare cheque distribution, and the association between monthly payments and mental health and substance-related ED presentations. METHOD: The Vancouver Police administrative database was analyzed during an 81-week period (June 8, 2011, to December 25, 2012). Comparisons were made between the numbers of MHAs by police during the week following welfare payment to those during nonpayment weeks. The weekly number of mental health and substance-related ED presentations were also analyzed during the study period. MHAs were analyzed continuously, and compared using the 2-tailed t test. RESULTS: During the study period, 4009 MHAs occurred (range 1 to 18 MHAs/day). The mean weekly MHAs during welfare week was 54.6 (95% CI 51.75 to 57.45), compared with 48.6 (95% CI 46.35 to 50.85) during nonpayment weeks (P = 0.004). This translates to 85 MHAs annually related to welfare payments. Total mental health and addictions-related presentations to the ED were also significantly increased in the week following welfare payments (P < 0.001), and could not be solely attributed to increased MHAs by police. CONCLUSION: A statistically significant increase in the number of MHAs by police follows welfare payments. This is superimposed on a significant increase in overall mental health and substance-related ED presentations seen during the same period.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".