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Record W2947841679 · doi:10.1186/s12913-019-4088-z

Health service brokerage to improve primary care access for populations experiencing vulnerability or disadvantage: a systematic review and realist synthesis

2019· review· en· W2947841679 on OpenAlexfundno aff
Louise Thomas, Sharon Parker, Hyun Jung Song, Nilakshi Gunatillaka, Grant Russell, Mark Harris

Bibliographic record

VenueBMC Health Services Research · 2019
Typereview
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchUniversity of New South WalesAustralian GovernmentMcGill UniversityFonds de Recherche du Québec - SantéMonash UniversityLa Trobe UniversityUniversity of OttawaAustralian Primary Health Care Research Institute, Australian National UniversityPrimary Health Care Research, Evaluation and Development
KeywordsNursing researchHealth informaticsDisadvantageHealth administrationVulnerability (computing)MedicinePublic healthNursingService (business)Health economicsHealth services researchPrimary careFamily medicineBusinessComputer securityPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Individuals experiencing disadvantage or marginalisation often face difficulty accessing primary health care. Overcoming access barriers is important for improving the health of these populations. Brokers can empower and enable people to access resources; however, their role in increasing access to health services has not been well-defined or researched in the literature. This review aims to identify whether a health service broker working with health and social service providers in the community can (a) identify individuals experiencing vulnerability who may benefit from improved access to quality primary care, and (b) link these individuals with an appropriate primary care provider for enduring, appropriate primary care. METHODS: Six databases were searched for studies published between January 2008 and August 2015 that evaluated a health service broker intervention linking adults experiencing vulnerability to primary care. Relevant websites were also searched. Included studies were analysed using candidacy theory and a realist matrix was developed to identify mechanisms that may have contributed to changes in response to the interventions in different contexts. RESULTS: Eleven studies were included in the review. Of the eight studies judged to provide detailed description of the programs, the interventions predominately addressed two domains of candidacy (identification of candidacy and navigation), with limited applicability to the third and fourth dimensions (permeability of services and appearances at health services). Six of the eight studies were judged to have successfully linked their target group to primary care. The majority of the interventions focused on assisting patients to reach services and did not look at ways that providers or health services could alter the way they deliver care to improve access. CONCLUSIONS: While specific mechanisms behind the interventions could not be identified, it is suggested that individual advocacy may be a key element in the success of these types of interventions. The interventions were found to address some dimensions of candidacy, with health service brokers able to help people to identify their need for care and to access, navigate and interact with services. More consideration should be given to the influence of providers on patient candidacy, rather than placing the onus on patients.

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)0.025
metaresearch head score (Gemma)0.092
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 score0.025
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.092
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0110.012
Bibliometrics0.0150.015
Science and technology studies0.0010.002
Scholarly communication0.0060.004
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.245
GPT teacher head0.570
Teacher spread0.325 · 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

Citations30
Published2019
Admission routes1
Has abstractyes

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