MétaCan
Menu
Back to cohort

1775 Development of a national hub for reviewing and learning from the deaths of children and young people in Scotland

2021· article· en· W3204531672 on OpenAlexaboutno aff
Nanisa Feilden, Alison Rennie, Jill Sands, Caroline McGeachie, Sharon E. Robertson

Bibliographic record

VenueAbstracts · 2021
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDocumentationQuality (philosophy)Quarter (Canadian coin)Consistency (knowledge bases)Action (physics)Health careQuality managementChild mortalityPublic relationsMedical educationPopulationEnvironmental healthEconomic growthMarketingComputer science

Abstract

fetched live from OpenAlex

<h3>Background</h3> Scotland has a higher mortality rate for under 18s than any other Western European country. Of the 300 children and young people who die annually, approximately a quarter could be prevented. There is currently no national system to support review or to share national learning, and not all deaths are reviewed. The quality of reviews varies across services and Scotland. Healthcare Improvement Scotland and the Care Inspectorate co-host the National Hub for Reviewing and Learning from the Deaths of Children and Young People. <h3>Objectives</h3> The National Hub aims to ensure that the death of every child in Scotland is subject to a quality review by: developing a methodology and documentation to ensure all deaths are reviewed through a high quality and consistent process improving the quality and consistency of existing reviews improving the experiences of and engagement with families and carers, and channelling learning from current review processes across Scotland that could direct action to help reduce preventable deaths. This programme reflects the commitment to fostering a learning system that increases safety and quality improvement amongst services by: supporting individuals to learn through its culture and networks ensuring everyone is informed by evaluation and reflective practice enabling people to assess what is and is not working through the use of qualitative and quantitative data, stories and insights developing processes to aid decision-making and turn knowledge into action building systems to identify ’bright spots’ and generalisable learning, and linking with rUK systems to allow a four nations approach to child death reviews. <h3>Methods</h3> We have worked collaboratively with stakeholders to support implementation of a national child death review process, which will launch during 2021. This includes: establishing an Expert Advisory Group to provide an advisory role through expert (including clinical) input developing national guidance that sets out the process for NHS boards and local authorities to follow when responding to, and reviewing, the death of a child or young person developing a core review data set and online portal for collating data, and gathering views from family members and carers who have been involved in a review process. Reviews will be conducted into the deaths of all live born children up to the date of their 18th birthday, or 26th birthday for care leavers who are in receipt of aftercare or continuing care at the time of their death <h3>Results</h3> The national child death review process will be fully implemented by 1 October 2021. Following implementation, the National Hub will collate and disseminate learning from reviews with the aims of changing future professional clinical practice, informing policy change and reducing avoidable deaths in Scotland. <h3>Conclusions</h3> For the first time in Scotland, national data will be collected on the deaths of all children and young people. Working with NHS boards and local authorities, the ambition is to inform the redesign of pathways and services to ultimately reduce avoidable deaths, and where that is not possible, to improve the experiences of children, young people and their families

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.328
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAbstractsSame topicGrief, Bereavement, and Mental HealthFrench-language works237,207