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Record W2294984738 · doi:10.1177/0009922815623498

Social Risk Screening for Pediatric Inpatients

2015· review· en· W2294984738 on OpenAlexafffund
Nikhil Pai, Sharmilaa Kandasamy, Elizabeth Uleryk, Jonathon L. Maguire

Bibliographic record

VenueClinical Pediatrics · 2015
Typereview
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsInstitute for Clinical Evaluative SciencesHospital for Sick ChildrenSickKids FoundationUniversity of TorontoMcMaster Children's HospitalMcMaster UniversitySt. Michael's Hospital
FundersHospital for Sick Children
KeywordsChecklistMedicineTheme (computing)Content validityQuality (philosophy)Family medicineApplied psychologyPsychometricsClinical psychologyPsychology

Abstract

fetched live from OpenAlex

This systematic review aims to identify existing social risk screening instruments applicable to hospitalized children (primary) and evaluate their content validity and methodological quality (secondary). Individual questions were abstracted and sorted by social risk theme. Content validity was evaluated by 13 hospital-based social workers. Methodological quality was assessed using the 108-item Consensus Based Standards for the Selection of Health Measurement Instruments (COSMIN) checklist. A total of 1070 citations were evaluated and 146 articles were reviewed, which identified 44 unique instruments. No instrument was applicable to social risk in hospitalized children. Sixty-one percent of instruments focused on a single social risk theme and only 18% of instruments covered more than 5 themes. The 2 instruments with the highest combination of social worker endorsement and COSMIN scores each addressed only 1 social risk theme relevant to hospitalized children. A broad, content valid and methodologically strong social risk screening instrument for hospitalized children was not identified.

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)0.016
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0070.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.364
GPT teacher head0.534
Teacher spread0.170 · 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

Citations26
Published2015
Admission routes2
Has abstractyes

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