Child Maltreatment and Public Health: Do Gaps in Response during the COVID-19 Pandemic Highlight Jurisdictional Complexities?
Bibliographic record
Abstract
Objective: Countermeasures introduced during the COVID-19 pandemic produced an environment that placed some children at increased risk of maltreatment at the same time as there were decreased opportunities for identifying and reporting abuse. Unfortunately, coordinated government responses to address child protection since the start of the pandemic have been limited in Canada. As an exploratory study to examine the potential academic evidence base and location of expertise that could have been used to inform COVID-19 pandemic response, we undertook a review of child maltreatment research across three prominent Canadian professional journals in social work, medicine and public health. Methods: We conducted a pre-pandemic, thirteen-year (2006–2019) archival analysis of all articles published in the Canadian Social Work Review (CSWR), the Canadian Medical Association Journal (CMAJ) and the Canadian Journal of Public Health (CJPH) and identified the research articles that related directly to child maltreatment, child protection or the child welfare system in Canada. Results: Of 11,824 articles published across the three journals, 20 research papers relating to child maltreatment, child protection or the child welfare system were identified (CJPH = 7; CMAJ = 3; CSWR = 10). There was no obvious pattern in article topics by discipline. Discussion: Taking these three prominent professional journals as a portal into research in these disciplines, we highlight the potential low volume of academic child maltreatment research despite the importance of the topic and irrespective of discipline. We believe that urgent transdisciplinary collaboration and overall awareness raising for child protection is called for at the time of the COVID-19 pandemic as well as beyond in Canada.
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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 | 0.050 | 0.174 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.022 | 0.043 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".