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Research Review: Economic evidence for interventions in children's social care: revisiting the What Works for Children project

2010· article· en· W2146976527 on OpenAlexaff
Madeleine Stevens, Helen Roberts, Alan Shiell

Bibliographic record

VenueChild & Family Social Work · 2010
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of Calgary
FundersEconomic and Social Research Council
KeywordsPsychological interventionIntervention (counseling)Economic evaluationPsychologyInclusion (mineral)Cost effectivenessMedical educationApplied psychologyPublic relationsMedicineSocial psychologyPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

ABSTRACT Evidence about the cost‐effectiveness of interventions in children's services can help decision‐makers make more efficient use of scarce resources. We returned to six somewhat disparate interventions on which we had collated research evidence identified by service planners and practitioners as relevant to the well‐being of children in the course of the Economic and Social Research Council‐funded What Works for Children project. These are home visiting, parenting, cognitive–bahavioural therapy, mentoring, traffic calming and breakfast club interventions. We aimed to explore the nature and extent of evidence on cost‐benefit and cost effectiveness for these measures. We conducted searches for studies that looked at the costs as well as the effectiveness of the six interventions and found 24 studies matching our inclusion criteria. The studies were diverse in terms of study design and economic methods (including economic modelling and willingness to pay). Studies relating to parenting programmes and traffic calming gave the most positive indication that the interventions may be cost‐effective for the outcomes in question. The remainder of the studies did not give a clear picture, in large part because of a lack of demonstration that the intervention was effective.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0070.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.004
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 head0.260
GPT teacher head0.534
Teacher spread0.274 · 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.

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

Citations10
Published2010
Admission routes1
Has abstractyes

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