Considerations for an Access-Centered Design of the Fever Thermometer in Low-Resource Settings: A Literature Review
Bibliographic record
Abstract
BACKGROUND: The lack of adequate information about fever in low-resource settings, its unreliable self-assessment, and poor diagnostic practices may result in delayed care and under-or-overdiagnosis of diseases such as malaria. The mismatches of existing fever thermometers in the context of use imply that the diagnostic tools and connected services need to be studied further to address the challenges of fever-related illnesses and their diagnostics. OBJECTIVE: This study aims to inform a product-service system approach to design a reliable and accessible fever thermometer and connected services, as well as contribute to the identification of innovative opportunities to improve health care in low-resource settings. METHODS: To determine what factors impede febrile people seeking health care to access adequate fever diagnostics, a literature search was conducted in Google Scholar and PubMed with relevant keywords. Next, these factors were combined with a patient journey model to design a new product-service system for fever diagnostics in low-resource settings. RESULTS: In total, 37 articles were reviewed. The five As framework was used to categorize the identified barriers. The results indicate that there is a poor distribution of reliable fever diagnostic practices among remote communities. This paper speaks to the global public health and design communities. Three complementary considerations are discussed that support the idea of a more holistic approach to the design of fever diagnostics: (1) understanding of the fever diagnostics patient journey, (2) identifying user groups of the thermometers in a specific health care system, and (3) assessing different needs and interests of the different users. CONCLUSIONS: Access to basic, primary health care may be enhanced with better information and technology design made through the involvement of system users.
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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 | 0.012 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".