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Record W971608215 · doi:10.1093/pch/19.4.195

From office tools to community supports: The need for infrastructure to address the social determinants of health in paediatric practice

2014· review· en· W971608215 on OpenAlexaff
Fatima Fazalullasha, Jillian Taras, Julia Morinis, Leo Levin, Karima Karmali, Barbara Neilson, Barbara Muskat, Gary Bloch, Kevin Chan, Maureen McDonald, Sue Makin, Elizabeth Ford-Jones

Bibliographic record

VenuePaediatrics & Child Health · 2014
Typereview
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsToronto Public HealthChildren's Aid SocietyCentre for Global Health ResearchSickKids FoundationUniversity of TorontoHospital for Sick ChildrenUniversity of Ottawa
Fundersnot available
KeywordsReferralPsychological interventionMedicineCommunity healthNursingPublic healthSocial determinants of healthHealth carePublic relationsFamily medicineMedical educationPolitical science

Abstract

fetched live from OpenAlex

Previous research has highlighted the importance of addressing the social determinants of health to improve child health outcomes. However, significant barriers exist that limit the paediatrician's ability to properly address these issues. Barriers include a lack of clinical time, resources, training and education with regard to the social determinants of health; awareness of community resources; and case-management capacity. General practice recommendations to help the health care provider link patients to the community are insufficient. The objective of the current article was to present options for improving the link between the office and the community, using screening questions incorporating physician-based tools that link community resources. Simple interventions, such as routine referral to early-year centres and selected referral to public health home-visiting programs, may help to address populations with the greatest needs.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.177
GPT teacher head0.501
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations32
Published2014
Admission routes1
Has abstractyes

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