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Record W4413879684 · doi:10.1001/amajethics.2025.642

How Should Clinicians Follow Up About Nonresponses to Mandatory SDoH Screening Questions?

2025· article· en· W4413879684 on OpenAlexaff
Audriana Mooth

Bibliographic record

VenueThe AMA Journal of Ethic · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsPolitical scienceMedicineFamily medicinePsychology

Abstract

fetched live from OpenAlex

Structural determinants of health (SDoH) screening is key to good pediatric care, but fear of life-altering consequences can prevent adults from disclosing information, while time constraints disincentivize clinicians from addressing some awkward but important SDoH topics relevant to good care planning and management.Transparency, clarity, and a nonjudgmental attitude can help cultivate safe multidisciplinary communication and openness during a clinical encounter.Even more important than screening for SDoH is responding to children's unmet needs that screening reveals, which is the focus of this commentary on a case.The American Medical Association designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 Credit available through the AMA Ed Hub TM .Physicians should claim only the credit commensurate with the extent of their participation in the activity.Case JJ is a single parent of twins, who is recently unemployed and struggling to cover costs of everyday living.When JJ takes their child to a family medicine physician, Dr P, for the child's annual physical examination, Dr P asks JJ to complete a form.One question, Do you have trouble paying utility bills?, makes JJ nervous about answering honestly, due to worry that they will be perceived as incapable of caring well for their child.JJ leaves the response area to this question blank.Dr P notices this omission but must enter information into the child's electronic health record as part of the structural determinants of health (SDoH) screening required by the state's Medicaid managed care plan.Dr P considers how to bring up JJ's lack of response to this screening question. CommentaryCases like this one are common in primary care and reflect resource distribution inequity that affects SDoH.Poverty and other SDoH are correlated with increased pediatric morbidity and mortality, including increased rates of hospitalization for acute as well as chronic illnesses. 1,2,3,4Addressing these SDoH can improve the health and well-being of children and families. 1,2,3,4Despite the fundamental impact that SDoH have on patients' health, patients' social needs can be a difficult topic to address-from the

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.093
GPT teacher head0.393
Teacher spread0.301 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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