Is patient navigation a solution to the problem of "leaving no one behind"? A scoping review of evidence from low-income countries
Bibliographic record
Abstract
Patient navigation interventions, which are designed to enable patients excluded from health systems to overcome the barriers they face in accessing care, have multiplied in high-income countries since the 1990s. However, in low-income countries (LICs), indigents are generally excluded from health policies despite the international paradigm of universal health coverage (UHC). Fee exemption interventions have demonstrated their limits and it is now necessary to act on other dimensions of access to healthcare. However, there is a lack of knowledge about the interventions implemented in LICs to support the indigents throughout their care pathway. The aim of this paper is to synthesize what is known about patient navigation interventions to facilitate access to modern health systems for vulnerable populations in LICs. We therefore conducted a scoping review to identify all patient navigation interventions in LICs. We found 60 articles employing a total of 48 interventions. Most of these interventions targeted traditional beneficiaries such as people living with HIV, pregnant women and children. We utilized the framework developed by Levesque et al. (Patient-centred access to health care: conceptualising access at the interface of health systems and populations. Int J Equity Health 2013;12:18) to analyse the interventions. All acted on the ability to perceive, 34 interventions on the ability to reach, 30 on the ability to engage, 8 on the ability to pay and 6 on the ability to seek. Evaluations of these interventions were encouraging, as they often appeared to lead to improved health indicators and service utilization rates and reduced attrition in care. However, no intervention specifically targeted indigents and very few evaluations differentiated the impact of the intervention on the poorest populations. It is therefore necessary to test navigation interventions to enable those who are worst off to overcome the barriers they face. It is a major ethical issue that health policies leave no one behind and that UHC does not benefit everyone except the poorest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".