A qualitative systematic review of barriers and facilitators to the implementation of community-based molecular diagnostics for infectious diseases
Bibliographic record
Abstract
BACKGROUND: Community-based molecular diagnostic testing for infectious diseases can bring equitable healthcare to resource-limited settings without hospital-based laboratories. Low-complexity molecular testing devices allow for unprecedented sensitivity and specificity for infectious disease diagnostics outside of a dedicated laboratory, which may facilitate timely initiation of therapeutics and other public health interventions in rural, remote and/or marginalized communities. OBJECTIVE: To identify barriers and facilitators to the implementation of community-based molecular testing. METHODS: A systematic search was conducted on MEDLINE Ovid, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health, Google Scholar, and reference lists. Original research that includes implementation of molecular testing systems at the community-level or at rural/remote health facilities with frontline healthcare workers and reports barriers and facilitators to implementation were included. Studies were assessed by the Critical Appraisal Skills Programme Qualitative Checklist and underwent inductive thematic analysis. The review protocol was registered to Prospero prior to conducting the review (CRD42023397800). RESULTS: A total of 6 studies were included in the review. We found three main themes present across all six included studies: infrastructure, usability, and staffing considerations. Infrastructure emerged as a critical determinant, with challenges ranging from physical space constraints to issues with reliable electricity and internet connectivity. The usability of testing devices, encompassing factors like ease of use and testing quality, also played a pivotal role. Staffing considerations, including workload, training, and attitudes, significantly influenced implementation outcomes. CONCLUSION: Our review highlights the importance of addressing infrastructural challenges, ensuring usability of testing devices, and adequately supporting staff through training and workload management to realize the full potential of this new opportunity. Future implementation should consider these factors to successfully integrate molecular diagnostics into community-level healthcare delivery, particularly in rural and remote areas.
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 | 0.020 |
| 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".