Workforce and welfare: Linking child protection staffing levels to recurrence risk
Bibliographic record
Abstract
Background Recurrent involvement with a child protection (CP) system may indicate a failure of the system to intervene effectively following an initial CP report. While there are several ecological, socioeconomic, and organizational factors that may influence whether a family experiences recurrence, not much is known about the relationship of CP staffing levels to the likelihood of recurrence. Objective This study employs data from across geographies of Quebec to explore whether the number of child protection workers responsible for evaluating reported concerns was associated with the likelihood of recurrence. We hypothesized that a higher number of workers would decrease the likelihood of recurrent substantiations during the study period. Participants and Setting Anonymized administrative CP data on first and subsequent substantiated reports were drawn from the Quebec CP system spanning the years 2000 to 2017. The study sample consisted of 720,681 children who were evaluated for the first time during the study period, of whom 25% (n =176,555) experienced a recurring substantiated investigation prior to age 18 or the end of the observation window. Annual levels of evaluation staffing levels for the CP agencies were extracted for the same time period and linked to the administrative dataset. Methods Using a multilevel model, this study tested whether the number of CP evaluation workers per capita in a family’s service delivery area was correlated to the likelihood of recurrent substantiation. Findings After controlling for geographically measured socioeconomic vulnerability, we found a significant correlation between lower child population staffing levels and an increased likelihood of recurrent substantiation.”. The residual variance was 28.5% lower for the model including child population staffing levels. Conclusion Results of the analysis demonstrated a significant role of staffing levels in determining risk of recurrent substantiation in Quebec. More research is needed to understand the mechanisms underlying this relationship to better inform practice and policy improvements.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".