Reforming Sri Lanka's child protection workforce: Lessons from global models of coordinated division of labor.
Bibliographic record
Abstract
Background: Child protection requires a cooperative division of labor to ensure children's safety and well-being are sustained. Sri Lanka's child protection workforce, however, suffers from issues such as role conflict, poor training, and lacking inter-agency collaboration, creating delay and risk accumulation for children. This investigation examines these within Sri Lanka's child protection system and contrasts them with international best practice in division of workforce. Method: This qualitative study is based on a document review of Sri Lankan child protection reports, legislation, and policies from 2014 to 2024. Additionally, child protection frameworks in Canada, Sweden, Australia, and the UK were reviewed. Thematic analysis was used to explore the roles of health services, police, and social services in safeguarding children. Results: The study identifies several gaps in Sri Lanka’s child protection system, including unclear role definitions and limited specialist training. In comparison, countries such as the UK, Australia, and Sweden have established effective systems including multi-disciplinary teams, case-linked management, Standard Operating Procedures (SOPs), Child Protection Information Management Systems (CPIMS), risk assessment tools, helplines/hotlines, and robust monitoring and evaluation mechanisms. These tools support timely and coordinated responses to child protection concerns. Sri Lanka’s system currently lacks such integrated tools and referral pathways, which hinders effective collaboration among stakeholders. Conclusions: The Sri Lankan child protection system must be supported by clearer roles, professional capacity-building and better inter-agency coordination. Utilizing best global practice, such as integrated case management and multi-disciplinary teams, can help strengthen Sri Lanka's system through the timely response and enhanced child protection.
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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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".