How could patient navigation help promote health equity in sub-Saharan Africa? A qualitative study among public health experts
Bibliographic record
Abstract
The indigents have long been excluded from health policies in sub-Saharan Africa. Despite recent efforts by some countries to allow them free access to health services, they face a multitude of non-financial barriers that prevent them from accessing care. Interventions to address the multiple patient-level barriers to care, such as patient navigation interventions, could help reverse this trend. However, our scoping review showed that no navigation interventions in low-income countries targeted the indigents. The objective of this qualitative study is, therefore, to go beyond the lack of evidence and discuss relevant approaches to act in favor of health care equity. We interviewed 22 public health experts with the objective of finding out which actions related to patient navigation programs (identified in the scoping review for other target groups) could be relevant and/or adapted for the indigents. For each ability to access care described by Levesque and colleagues, we were thus able to list the potential opportunities and challenges of implementing each type of action for the indigents in sub-Saharan Africa. Overall, the experts all felt that patient navigation programs were very relevant to implement for the indigents. They emphasized the need for personalized follow-up and for holistic actions to consider the whole context of the situation of indigence. The recommendations made by the experts are valuable in guiding political decision-making, while leaving room for adaptation of the proposed guidelines according to different contexts.
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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 | 0.030 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".