Multi-agency safeguarding: From everyone’s responsibility to a collective responsibility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multi-agency collaboration (also termed inter-professional, inter-agency, and multi-sector) between agencies and practitioners has been established as a valuable way of working in safeguarding, to protect people from harm. Whilst multiagency working is mandated in legislation, policy, and guidance, there are challenges in its implementation. Research has not only highlighted many benefits of multi-agency working, for example, sharing resources and expertise, but also key barriers, including uncertainty of agency roles, remits, and responsibilities. Ongoing challenges, such as information sharing in an appropriate and timely manner, are often cited within various serious practice reviews and inspections. However, what is less explored and understood is how we know and evidence if our multi-agency safeguarding arrangements are effective. This article summarizes the multi-agency safeguarding landscape and highlights an urgent need for the development of a framework that identifies key components to evidence effectiveness. This framework should seek to define, identify, monitor, and review factors that enable effective multi-agency partnership working. In doing so, we argue that the evidence of practice needs to build on safeguarding being “everyone’s responsibility” towards establishing a “collective responsibility.” This is the first of the two papers mapping developmental journey of “The Collective Safeguarding Responsibility Model: 12Cs”. Safeguarding; Multi-Agency; Inter-Agency; Partnership; Model; Cooperation; Collaboration; Vulnerability.
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.
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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it