Introduction of Mobile Health Tools to Support COVID-19 Training and Surveillance in Ogun State Nigeria
Bibliographic record
Abstract
Mobile health (mhealth) tools delivered through wireless technology are emerging as effective strategies for delivering quality training, ensuring rapid clinical decision making, and monitoring implementation of simple and effective interventions in under-resourced settings. We share our early experience of developing and deploying the InStrat COVID-19 health worker training application (App) in Ogun State, Western Nigeria where the country's first COVID-19 case was reported. This App was designed to directly provide frontline health workers with accurate and up-to-date information about COVID-19; enable them to quickly identify, screen and manage COVID-19 suspects; provide guidance on specimen collection techniques and safety measures to observe within wards and quarantine centers dealing with COVID-19. The App was deployed in 271 primary health care facilities in Ogun state and a total of 311 health workers were trained to use it. Of the 123 health workers who completed knowledge pre- and post-tests, their average test score improved from 47.5 (±9.4) to 73.1(±10.0) %, P < 0.0001 after using the tutorial. Rapid adoption and uptake were driven largely by public-private sector involvement as well as certification of health workers with reported satisfaction levels of over 95% among those who completed pre- and post-test surveys. Challenges encountered included a lack of universal availability of android phones for frontline health workers, lack of internet access in remote areas and a need to incentivize the workers. The timely deployment of this App targeting primary health care workers, mostly in hard-to-reach areas, obviated the need for conventional didactic training with potential of savings in training costs and time and could be applied to similar contexts.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".