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Record W7118109134

Healthcare interventions to improve health outcomes for racially minoritised people with multiple long-term conditions: A Systematic Review and Narrative synthesis

2025· preprint· W7118109134 on OpenAlexaboutno aff
Sakib Tariq, Mavin Nathan Kashyap, Brenda Hayanga, Patrick Oyibo, Kathryn Hartley, Laia Bécares, Mengxing Joshi, Rida Sarwar

Bibliographic record

VenueSocArXiv (OSF Preprints) · 2025
Typepreprint
Language
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionHealth careInclusion (mineral)Multidisciplinary approachMental healthHealth equityMEDLINENarrative
DOInot available

Abstract

fetched live from OpenAlex

Racially minoritised people with multiple long-term conditions (MLTCs) face inequalities across different dimensions of health(care), yet little is known about how to improve their health(care) outcomes. This systematic review and narrative synthesis seeks to identify and describe healthcare interventions designed to improve health outcomes for racially minoritised people with MLTCs and identify areas for further exploration. Given that primary care is considered the ideal setting to manage MLTCs, we focus on interventions targeted at healthcare providers/systems. We searched 9 bibliographic databases and one website and identified 6566 studies, 15 of which met the inclusion criteria. The studies were conducted in the US (n=13), Canada (n=1) and Australia (n=1). Most studies recruited racially minoritised people mainly of African American and Hispanic/Latinx descent with comorbid depression and a physical condition (diabetes (n=3), hypertension (n=3), cancer (n=2). Depression/mental health outcomes, patient-reported outcomes, clinical outcomes, medication use, and adherence were the most frequently assessed outcomes. All interventions made socio-cultural adaptations, thereby, promoting equitable and inclusive care. Community actors/assets were considered key to improving health outcomes. Of the 15 interventions, five resulted in statistically significant improvements in all outcomes of interest and nine resulted in improvements in some outcomes. This review illustrates the feasibility of socio-culturally adapted interventions, many of which successfully integrate physical and mental health care, delivered through multidisciplinary teams working collaboratively, and leveraging community assets to improve health outcomes for racially minoritised people with MLTCs. Future research is needed to assess the impact of these interventions beyond North America and Australia.

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)0.018
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.077
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0080.009
Bibliometrics0.0120.012
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.039
GPT teacher head0.374
Teacher spread0.334 · 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 designSystematic review
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

Citations0
Published2025
Admission routes1
Has abstractyes

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