An Assessment of Healthcare Relationship Trust between Patent Medicine Vendors and Residents of Hard-to-Reach Settlements in Northern Nigeria
Bibliographic record
Abstract
A trusting relationship is required for effective communication between care providers and care seekers, and trust is a determinant of early healthcare-seeking and care outcomes. The patient-Doctor healthcare trust relationship has been widely studied in different settings. However, there is a dearth of information on whether the factors underlying healthcare relationship trust between patients and their doctors are the same as those underlying patients’ trust in other healthcare professionals (including the PPMVs). This study, therefore, aims to assess the level and determinants of healthcare relationship trust between patent medicine vendors and their clients in hard-to-reach settlements in North-western Nigeria. We adapted the revised healthcare relationship trust scale, administered by trained data collectors using android devices. The data were analysed using Stata (version 16). We used the Chi-square test to identify the correlates of the level of trust(high/low), and binary logistic regression was used to identify its determinants. Statistical significance was defined as P<0.05. Slightly above one-quarter (28.1%) of the underserved had high healthcare relationship trust in the PPMVs. Being married, male, residing in a rented or makeshift shelter in Kaduna state, with under-five child(ren) in their household, and self-reporting good health predicted a high level of healthcare relationship trust in PPMVs among the underserved who participated in this study. We concluded that the personal and household characteristics of the underserved could significantly influence their level of trust in PPMV. Therefore, to achieve the aim of improving health outcomes in deprived populations, initiatives seeking to integrate PPMVs into the formal health system in resource-constrained settings should seek to address the determinants of healthcare relationship trust in these populations as part of their rollout process. Keywords: Communities, Hard-to-reach, Healthcare, Trust, Relationship, Workforce.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".