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Record W4296430585 · doi:10.1093/pch/21.supp5.e91

The First Step to Helping: Asking About Poverty

2016· article· en· W4296430585 on OpenAlexaffabout
J Teicher, J Ysselstein Qadri, G Bloch, J Orkin

Bibliographic record

VenuePaediatrics & Child Health · 2016
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsToronto Public Health
Fundersnot available
KeywordsPovertySocial determinants of healthAgency (philosophy)Health equityHealth careGrounded theoryCommunity healthPublic healthQualitative research

Abstract

fetched live from OpenAlex

Abstract BACKGROUND: Canadian children living in poverty are more likely to experience poor health outcomes. Physicians have a unique opportunity to screen for poverty and other social determinants of health (SDOH) in order to intervene early and change their patients' health trajectories. It is well known in the literature to date that addressing SDOH in clinical practice can improve health outcomes, however, significant barriers have been identified that limit a physician’s ability to address these issues. The Child Poverty Assessment Tool (CPAT) was developed by an inter-professional team of physicians, social workers and community child health agency partners to provide healthcare providers with screening questions for the SDOH and resources to address identified SDOH needs. The tool was created to support physicians with links to the community and simple screening approaches to common social issues. OBJECTIVES: To develop an understanding of physicians' current SDOH screening practices and attitudes to screening for the SDOH in clinical practice; To explore the feasibility, accessibility, and relevance of the CPAT for paediatricians at an academic health science centre. DESIGN/METHODS: Using a qualitative grounded theory approach, seven consultant academic paediatricians were individually interviewed. The interviews were conducted using a semi-structured interview guide, and were digitally recorded and transcribed verbatim. Two team members independently coded the transcripts for recurrent themes. This project was undertaken as a Quality Improvement project and was conducted with appropriate ethical approval. RESULTS: Three major themes emerged regarding benefits of screening for SDOH: improved assessment of social issues, increased referrals to community supports and agencies, and appropriate modifications to treatment plans as informed by social issues. In addition to limiting billing models and time constraints, five major themes were identified as challenges to screening for SDOH: lack of knowledge of resources, upsetting family expectations, the physician’s own comfort in asking questions regarding the SDOH, the biomedical model of training, and physicians' understanding of their scope of practice. The CPAT was found to address some, but not all of these challenges. CONCLUSION: The results from the project elucidated important factors that influence the SDOH screening practices of paediatricians. While a structured tool (i.e. the CPAT) may provide support to physicians conducting screening, systemic and medical cultural barriers exist. Further research is needed to examine the effectiveness of implementing screening tools in clinical practice, and to identify solutions for systemic barriers to screening for poverty in paediatric populations.

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)0.009
metaresearch head score (Gemma)0.017
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: Commentary · Consensus signal: none
Teacher disagreement score0.349
Threshold uncertainty score0.694

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0180.009
Scholarly communication0.0030.002
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.027
GPT teacher head0.346
Teacher spread0.319 · 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
GenreCommentary

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
Published2016
Admission routes2
Has abstractyes

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