Bibliographic record
Abstract
This paper compares and contrasts child death review (CDR) structures and processes in six countries – Australia, New Zealand, the United States, Canada, England and Wales. It presents findings from a comparative study based on analysis of data from 18 case studies. Data were collected through a combination of documentary analysis, interviews and observations. The study found that CDR processes vary according to: where the function is located and whether review is undertaken at state, local or national level; whether review is rooted in legislation; the focus of review; whether dedicated funding is provided; whether families are involved in the process; and whether structures are supported by useful data systems. It was not possible to evaluate the effectiveness of different review systems but the findings suggest that structure makes little difference in terms of determining the extent to which CDR findings inform prevention effort and activity. While factors such as lack of funding, lack of national data, or lack of legislation may hinder the work of CDR teams, CDR findings have informed prevention initiatives despite such barriers. Copyright © 2013 John Wiley & Sons, Ltd. ‘It presents findings from a comparative study based on analysis of data from 18 case studies’ Key Practitioner Messages Standardisation and aggregation of data at national or state level are crucial for effective CDR. A model of individual review, cross‐case review and themed review can result in real learning and practice change. A public health model of CDR offers the most potential in terms of prevention. Families can contribute key information but participation must be managed sensitively and take account of cultural issues. ‘A model of individual review, cross‐case review and themed review can result in real learning and practice change.’
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 | 0.052 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".