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Record W4377092483 · doi:10.1186/s12939-023-01900-y

Identifying facilitators and barriers to integrated and equitable care for community-dwelling older adults with high emergency department use from historically marginalized groups

2023· article· en· W4377092483 on OpenAlexafffund
Krystal Kehoe MacLeod, Karyle Nama Flores, Kavish Chandra

Bibliographic record

VenueInternational Journal for Equity in Health · 2023
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsSaint John Regional HospitalDalhousie UniversityUniversity of New BrunswickHorizon Health NetworkBruyèreUniversity of Ottawa
FundersNew Brunswick Innovation Foundation
KeywordsThematic analysisHealth services researchEmergency departmentHealth careHealth equityIntersectionalitySociologyGerontologySocial policyMedicineQualitative researchPublic healthNursingPolitical scienceGender studiesSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: High rates of emergency department (ED) use by older adults persist despite attempts to improve accessibility of appropriate and comprehensive care. Understanding the drivers of ED visits from the perspective of older adults from historically marginalized groups could help reduce ED use by patients with needs that are preventable or could have been treated in a more appropriate setting. This interpretivist, feminist study aims to explore the unmet care needs of older adults (age 65 +) with high ED use and belonging to historically marginalized groups to better understand how social and structural inequities reinforced by neoliberalism; federal and provincial governance structures and policy frameworks; and regional processes and local institutional practices, shape the experiences of these older adults, particularly those at risk of poor health outcomes based on the social determinants of health (SDH). METHODS/DESIGN: This mixed methods study will employ an integrated knowledge translation (iKT) approach, starting with a quantitative phase followed by a qualitative phase. Older adults self-identifying as belonging to a historically marginalized group, having visited an ED three or more times in the past 12 months, and living in a private dwelling, will be recruited using flyers posted at two emergency care sites and by an on-site research assistant. Data obtained through surveys, short answer questions, and chart review will be used to compile case profiles of patients from historically marginalized groups with potentially avoidable ED visits. Descriptive and inferential statistical analyses and inductive thematic analysis will be conducted. Findings will be interpreted using the Intersectionality-Based Policy Analysis Framework to identify the interconnections between unmet care needs, potentially avoidable ED admissions, structural inequalities, and the SDH. Semi-structured interviews will be conducted with a subset of older adults at risk of poor health outcomes based on SDH, family care partners, and health care professionals to validate preliminary findings and collect additional data on perceived facilitators and barriers to integrated and accessible care. DISCUSSION: Exploring the linkages between potentially avoidable ED visits by older adults from marginalized groups and how their care experiences have been shaped by inequities in the systems, policies, and institutions that structure health and social care provision will enable researchers to offer recommendations for equity-focused policy and clinical practice reforms to improve patient outcomes and system integration.

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)0.006
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0010.005
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 head0.073
GPT teacher head0.404
Teacher spread0.331 · 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 designQualitative
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

Citations5
Published2023
Admission routes2
Has abstractyes

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