Transaction costs of access to health care: Implications of the care-seeking pathways of tuberculosis patients for health system governance in Nigeria
Bibliographic record
Abstract
Health care costs incurred prior to the appropriate patient-provider transaction (i.e., transaction costs of access to health care) are potential barriers to accessing health care in low- and middle-income countries. This paper explores these transaction costs and their implications for health system governance through a cross-sectional survey of adult patients who received their first diagnosis of pulmonary tuberculosis (TB) at the three designated secondary health centres for TB care in Ebonyi State, Nigeria. The patients provided information on their care-seeking pathways and the associated costs prior to reaching the appropriate provider. Of the 452 patients, 84% first consulted an inappropriate provider. Only 33% of inappropriate consultations were with qualified providers (QP); the rest were with informal providers such as pharmacy providers (PPs; 57%) and traditional providers (TP; 10%). Notably, 62% of total transaction costs were incurred during the first visit to an inappropriate provider and the mean transaction costs incurred was highest with QPs (US$30.20) compared with PPs (US$14.40) and TPs (US$15.70). These suggest that interventions for reducing transaction costs should include effective decentralisation to integrate TB care with services at the primary health care level, community engagement to address information asymmetry, enforcing regulations to keep informal providers within legal limits and facilitating referral linkages among formal and informal providers to increase early contact with appropriate providers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".