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‘Intervening early’: agendas and rationalisations for children’s developmental health

2019· article· en· W2972261930 on OpenAlexaffabout
Céline Cressman, Fiona A. Miller, Astrid Guttmann, John Cairney, Robin Z. Hayeems

Bibliographic record

VenueEvidence & Policy · 2019
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of Toronto
Fundersnot available
KeywordsIntervention (counseling)PoliticsDiversity (politics)PopulationScientific evidenceHealth carePublic relationsPolitical sciencePsychologyNursingMedicineLawEnvironmental healthEpistemology

Abstract

fetched live from OpenAlex

Background: Despite broad scientific consensus about the importance of the early years in the lifelong health and wellbeing of children, there is debate about whether and how healthcare professionals can optimise early child development through monitoring or screening. The evidence in support of a systematic population-level intervention is disputed, which is reflected in the diversity of approaches to developmental screening internationally. Methods: Using a case-study design, and interpretive qualitative methods, we explored how Canadian experts in child health (n=39): a) rationalise why they do, or would, pursue population-level developmental screening; b) articulate the policy goals of such an intervention, and; c) justify the practice with reference to evidence. Findings: Respondents identified three distinct framings, or policy agendas, for what developmental screening can and should seek to achieve, specifically: 1) as medical intervention, facilitating the early identification of health risk or disorder; 2) as social intervention, providing an opportunity for communication and connection with parents for all children; and 3) as political intervention, staking a claim for early child health on the broader political agenda. Discussion and conclusions: Each agenda is justified by distinct types of evidence, posing a challenge to simplistic models of evidence-based policymaking, and demonstrating that evidence is not just an input, but a contested part of a dynamic and political policymaking process.

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)0.001
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 score0.077
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.114
GPT teacher head0.470
Teacher spread0.357 · 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

Citations1
Published2019
Admission routes2
Has abstractyes

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