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Social determinants of health—to screen or not to screen? How, when, and what to do next are really the questions.

2022· article· en· W4298139516 on OpenAlexaboutno aff
Rebekah Angove, Kathleen D. Gallagher

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSocial determinants of healthFamily medicineHealth careQuarter (Canadian coin)Social mediaInclusion (mineral)Breast cancerGerontologyPublic healthCancerNursingPsychologySocial psychology

Abstract

fetched live from OpenAlex

169 Background: Social determinants of health (SDOH) have a considerable impact on the health outcomes of chronically ill patients. Although the implementation of social needs screening in clinical settings has been studied, patient perspectives of discussing SDOH with health care providers has not been thoroughly investigated. This study sought to explore the experience and perspectives of limited-resource patients with cancer regarding SDOH discussions. Methods: This cross-sectional analysis used data from a nationwide survey distributed in May 2022 by Patient Advocate Foundation (PAF). The survey was fielded via email to patients who received PAF services in 2020. Inclusion criteria included a valid email, aged >19 and a current or previous cancer treatment. Frequencies and percentages were calculated for categorical variables. Questions focused on individual experiences with SDOH screening, conversations and expectation around information use and assistance. Results: A total of 481 survey respondents with cancer completed the survey. Most respondents were female (73%), aged 56-75 (52%), household income < $48,000 (66%), and insured (98%); 38% were Black, Indigenous or Persons of Color (BIPOC). The most common cancer types were hematologic (42%) and breast (34%); 30% were diagnosed < 2 years prior and 82% received treatment in past 6 months. One quarter (26%) stopped or delayed care in past 12 months due to cost and 66% reported that social needs interfered with treatment in past 12 months. Two-thirds (64%) reported conversations about social needs in the past 12 months. Transportation (36%), food insecurity (32%) and personal safety were the most cited nonmedical needs. Conversations were most often initiated by nurse/PA (30%), social worker (30%) or doctor (29%) and patients reported being ‘extremely comfortable’ being asked these questions by the same providers; doctor (54%), nurse/PA (48%), social worker (46%). Over half (53%) reported comfort with SDOH information being part of their medical record; 61% wanted to be asked SDOH questions face-to-face. If a social need was identified, patients trusted patient advocacy groups (64%), social worker (61%) and charitable non-profit organizations (49%) to help them locate assistance. Only 21% indicated knowledge of availability of needs navigation services. Conclusions: Although patients are open to sharing social issues with providers, our data suggests that conversations may not be routinely initiated in clinical settings. There is also a need to increase awareness of resources in response to SDOH screening. Challenges in trust and privacy persist when disclosing this information. Achieving health equity requires culturally responsive strategies to embed screening and referrals into workflow to ensure cancer patients’ needs are identified and they are linked to appropriate nonmedical resources and services.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.566
GPT teacher head0.627
Teacher spread0.062 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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