Child welfare service delivery via remote communication: Perspectives on engagement from service users and providers in Ontario, Canada
Bibliographic record
Abstract
The COVID-19 pandemic and subsequent physical distancing orders resulted in the implementation of most child welfare services in Ontario, Canada through remote or non-face-to-face communication. The widespread shift to remote service delivery was unprecedented and guided by few or no child welfare policies or experiences for workers or leaders to draw upon. While the current child welfare body of literature offers strategies for in-person relationship building and engagement, there is no evidence exploring the link between engaging service users and remote service delivery. Given this significant knowledge gap, this study explored service providers' and users’ experiences about whether and how engagement was impacted by providing or receiving child welfare services via remote communication. The study utilized a qualitative research design that included a sample of child welfare workers ( n = 15), caregivers ( n = 15), and youth ( n = 17); data were analyzed using thematic analysis. Results suggest that engagement can be promoted through remote communication, as it signals trust and respect, promoting connections and reduced fear. There were also challenges in using remote communication especially with young children, those with less technological experience, and for difficult conversations, but it should be considered for future use based on individual needs. Further strengths and challenges are discussed, including mitigating factors. • Remote service delivery can promote engagement through building trust and relationships. • Technical difficulties, privacy issues, and working with interpreters may limit service user engagement. • Future use of remote service delivery should be assessed individually and consider families' level of comfort with the system, as well as the maltreatment concern.
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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 | 0.001 | 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 | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".