Social benefit payments and acute injury among low-income mothers.
Bibliographic record
Abstract
BACKGROUND: Human error due to risky behaviour is a common and important contributor to acute injury related to poverty. We studied whether social benefit payments mitigate or exacerbate risky behaviours that lead to emergency visits for acute injury among low-income mothers with dependent children. METHODS: We analyzed total emergency department visits throughout Ontario to identify women between 15 and 55 years of age who were mothers of children younger than 18 years, who were living in the lowest socio-economic quintile and who presented with acute injury. We used universal health care databases to evaluate emergency department visits during specific days on which social benefit payments were made (child benefit distribution) relative to visits on control days over a 7-year interval (1 April 2003 to 31 March 2010). RESULTS: A total of 153 377 emergency department visits met the inclusion criteria. We observed fewer emergencies per day on child benefit payment days than on control days (56.4 v. 60.1, p = 0.008). The difference was primarily explained by lower values among mothers age 35 years or younger (relative reduction 7.29%, 95% confidence interval [CI] 1.69% to 12.88%), those living in urban areas (relative reduction 7.07%, 95% CI 3.05% to 11.10%) and those treated at community hospitals (relative reduction 6.83%, 95% CI 2.46% to 11.19%). No significant differences were observed for the 7 days immediately before or the 7 days immediately after the child benefit payment. INTERPRETATION: Contrary to political commentary, we found that small reductions in relative poverty mitigated, rather than exacerbated, risky behaviours that contribute to acute injury among low-income mothers with dependent children.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".