Health service brokerage to improve primary care access for populations experiencing vulnerability or disadvantage: a systematic review and realist synthesis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".