Referrals to child and family services during the COVID-19 pandemic: An analysis of administrative data from British Columbia and Northwest Territories, Canada
Bibliographic record
Abstract
BACKGROUND: Evidence about the impact of the pandemic on child and family services in Canada is limited. OBJECTIVES: To determine if patterns of referrals changed in relation to in-person school closures/re-openings during the pandemic compared with a pre-pandemic period and to assess variations in referral volumes by child age and sex, referral source, child maltreatment type, and service response. PARTICIPANTS AND SETTING: De-identified record-level administrative data from child and family services in British Columbia (BC) and Northwest Territories (NWT), Canada were analyzed. METHODS: We used frequencies and relative percent change (RPC) to assess monthly changes in referrals in the pre-pandemic and pandemic periods, stratified by age, sex, referral source, maltreatment type, and service response. We conducted t-tests for differences in the average number of referrals between each month in 2020/2021 versus the corresponding month in 2019 and used t-tests and Chi-square tests to examine the overall differences between the pre-pandemic 12-month period and the first 12 months of the pandemic. RESULTS: In BC, referrals to child and family services decreased significantly in April and May 2020 (RPC: 26.2 %, 22.6 %, respectively, p < 0.05) compared with the same months in 2019. This was followed by an upturn in June 2020, which was statistically similar to June 2019. The sharpest reduction was for referrals made by school/childcare personnel (RPC: 73.4 %, 73.8 %, respectively, p < 0.05). In NWT, patterns were similar. For both jurisdictions, the total number of referrals was not statistically different after schools re-opened in August-September 2020, compared with the same period in 2019. CONCLUSION: The decrease in all referrals and referrals from school/childcare personnel in BC and NWT appears to correspond with pandemic-related school closures early in 2020.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".