“Essential” services, risk, and child protection in the time of COVID-19: An opportunity to prioritize chronic need
Bibliographic record
Abstract
In many North American jurisdictions, socioeconomically vulnerable families are more likely to be involved with child protection systems and experience ongoing challenges. The current public health response to the COVID-19 pandemic is having a disproportionate impact on these families via unemployment, “essential” work, isolation, and closures of childcare and schools, with negative implications for children’s developmental wellbeing. Experts warn that while child protection referrals have gone down, children who are at risk of maltreatment are less exposed to typical reporters (e.g., school professionals). At the same time, physical distancing measures are prompting many human service settings to shift toward virtual intervention with children and families. In this commentary, we suggest that a focus on short-term risk in the response to COVID-19 may obscure support for children’s long-term outcomes. We propose two policy considerations: (1) in the immediate term, that child protection workers be deemed “essential”; and (2) in the longer term, that permanent, universal basic income guarantees be implemented to support a baseline of predictability both in families’ material wellbeing and in fiscal budgets in the case of a future crisis. As we write, it is impossible to predict the longevity of these closures nor the extent of their impact on children and families. However, the present article mirrors commentary following previous crises noting the importance of going beyond immediate health risk mitigation to consider wellbeing with regard to children’s development and families’ socioeconomic needs in the long term.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.020 | 0.033 |
| Insufficient payload (model declined to judge) | 0.004 | 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".