Challenges with implementing malaria rapid diagnostic tests at primary care facilities in a Ghanaian district: a qualitative study
Bibliographic record
Abstract
Rapid diagnostic Tests (RDTs) for malaria enable diagnostic testing at primary care facilities in resource-limited settings, where weak infrastructure limits the use of microscopy. In 2010, Ghana adopted a test-before-treat guideline for malaria, with RDT use promoted to facilitate diagnosis. Yet healthcare practitioners still treat febrile patients without testing, or despite negative malaria test results. Few studies have explored RDT implementation beyond the notions of provider or patient acceptability. The aim of this study was to identify the factors directly influencing malaria RDT implementation at primary care facilities in a Ghanaian district.Qualitative interviews, focus groups and direct observations were conducted with 50 providers at six purposively selected primary care facilities in the Atwima-Nwabiagya district. Data were analysed thematically.RDT implementation was hampered by: (1) healthcare delivery constraints (weak supply chain, limited quality assurance and control, inadequate guideline emphasis, staffing limitations); (2) provider perceptions (entrenched case-management paradigms, limited preparedness for change); (3) social dynamics of care delivery (expected norms of provider-patient interaction, test affordability); and (4) limited provider engagement in policy processes leading to fragmented implementation of health sector reform.Limited health system capacity, socio-economic, political, and historical factors hampered malaria RDT implementation at primary care facilities in the study district. For effective RDT implementation providers must be: (1) adequately enabled through efficient allocation and management of essential healthcare commodities; (2) appropriately empowered with the requisite knowledge and skill through ongoing, effective professional development; and (3) actively engaged in policy dialogue to demystify socio-political misconceptions that hinder health sector reform policies from improving care delivery. Clear, consistent guideline emphasis, with complementary action to address deep-rooted provider concerns will build their confidence in, and promote uptake of recommended policies, practices, and technology for diagnosing malaria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".