Preparedness of the Local Population for the Uptake of Artificial Intelligence and Digital One Health for Home Healthcare of Emerging and Reemerging Infectious Diseases in Southwest and Littoral Regions of Cameroon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Rapid digital responses to pandemics highlight advancements in healthcare, data sharing, and artificial intelligence (AI). While AI has driven progress in precision medicine, drug discovery, and vaccine development, its application to emerging and reemerging infectious diseases (ERIDs) remains underexplored, presenting critical challenges in addressing future health threats. Objectives: The study evaluated knowledge of ERIDs, AI, and Digital One Health (DOH) technologies, examined preparedness for their adoption in home healthcare, and identified factors influencing readiness to utilize these technologies in selected health districts of Cameroon. Methods: A cross‐sectional study assessed the preparedness of communities in Buea, Limbe, Bonassama, and New‐Bell Health Districts to adopt AI and DOH technologies from April to May 2024. Systematic random sampling included 33 communities, with data collected using face‐to‐face structured questionnaires. Analysis using SPSS Version 26 involved descriptive statistics and logistic regression, with statistical significance set at p < 0.05 and a 95% confidence interval to identify key associations. Results: Among 1625 participants, only 280 (17.2%) had good knowledge of ERIDs, with COVID‐19 (68.8%) and cholera (94.5%) being the most recognized examples. Knowledge of AI and DOH technologies was poor, with only 166 (10.2%) demonstrating accurate understanding. Early disease detection emerged as a critical application of AI for ERID control. Preparedness to adopt AI and DOH technologies was reported by 941 (57.9%), with 64.5% comfortable with AI‐generated interpretations and willing to use digital health tools during ERID outbreaks. Factors independently associated with preparedness included being a student (AOR = 2.678; 95% CI: 1.744–4.113; p < 0.001), good knowledge of AI and DOH (AOR = 7.141; 95% CI: 4.192–12.162; p < 0.001), and prior training on AI and digital health (AOR = 3.081; 95% CI: 2.272–4.179; p < 0.001). Conclusion: The study revealed insufficient knowledge of ERIDs, AI, and DOH but high preparedness to adopt these technologies for home care. Enhanced educational campaigns are recommended to improve community understanding and effective utilization of AI and DOH for controlling ERIDs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it