Understanding the roles of the healthcare and child welfare systems in promoting the safety and well-being of children
Bibliographic record
Abstract
Introduction: The accurate identification and appropriate investigation of child maltreatment is a key priority for promoting the optimal health and development of children. Healthcare providers are often well-positioned professionals to report suspected child abuse and neglect, and, therefore, interact regularly with child welfare workers. Little research has examined the relationship between these two groups of professionals. Methods: We interviewed healthcare providers and child welfare workers in order to examine the referral and child welfare investigation processes to understand strengths and identify areas of improvement for future collaboration. Thirteen child welfare workers from child welfare agencies and eight healthcare providers from a pediatric tertiary care hospital in Ontario, Canada were interviewed to meet the study's objectives. Results: Healthcare providers spoke about positive experiences making reports, factors impacting reporting decisions, areas for improvement (e.g., difficulties communicating, lack of collaboration, and disruption of therapeutic alliance), training, and professional roles. For interviews with child welfare workers, identified themes included healthcare professionals' perceived expertise and understanding the role of child welfare. Both groups brought up the need for increased collaboration as well as systemic barriers and legacies of harm. Discussion: Our core finding was a reported lack of communication between the groups of professionals. Other identified barriers in collaboration included a lack of understanding of each other's roles, hesitation for healthcare providers making reports, as well as legacies of harm and systemic inequities in both institutions. Future research should build on this examination by including the voices of healthcare providers and child welfare workers to identify sustainable solutions for increased collaboration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".