Socioeconomic risk and the longitudinal child lifetime prevalence of child protection involvement
Bibliographic record
Abstract
BACKGROUND: North American studies find that geographic indicators of disadvantage, such as concentrated poverty, significantly increase the risk of child protection involvement. Despite having one of the most extensive family support systems and progressive income redistribution policies in North America, the Canadian province of Québec still faces geographic variations in socioeconomic conditions that remain a major risk factor for child protection involvement. OBJECTIVE: This study asks how child protection involvement is distributed across socioeconomically distinct geographic areas of the province. Drawing from prior literature, we hypothesize that the highest level of child protection involvement across childhood (age 0-17) is found in the lowest socioeconomic areas. PARTICIPANTS & SETTING: This is a population-based prevalence study using administrative child protection data spanning the years 2000 to 2017 across Québec. METHODS: We constructed cumulative risk life tables of first instances of child protection events (report confirmation, compromised security or development, and out-of-home placement). Prevalence rates were mapped onto 10,650 Census dissemination areas divided into three tiers according to a validated socioeconomic status (SES) index. RESULTS: The highest childhood prevalence of confirmed child protection reports, finding of compromised security or development, and out-of-home placement was found in the lowest SES areas. Rates in low SES areas can be over twice the rates in high SES areas. CONCLUSIONS: Area-level socioeconomic vulnerability remains a robust predictor of child protection involvement even in a socially progressive context. Our findings underscore that without targeted pediatric and family services, as well as poverty-alleviation programs for high-need families in high-need areas, even well-intentioned systems may fall short of reaching the families most in need.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 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".