Development and evaluation of a mobile application trauma registry for use in low- and middle-income countries
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
Introduction Trauma registries are a means for improving trauma care in low- and middle-income countries, though a number of challenges for the sustainability of these trauma registries exist. Mobile health applications represent a promising technology for low- and middle-income country trauma registries. The development, implementation and evaluation of a mobile application trauma registry for use at the Mbarara Regional Referral Hospital, Uganda is demonstrated. Methods A paper-based trauma registry was implemented at the Mbarara Regional Referral Hospital. Based on feedback from local stakeholders, this was developed into an open-source mobile application version of the trauma registry. The mobile application was evaluated by 17 healthcare workers using a modified Unified Theory of Acceptance and Use of Technology questionnaire and qualitative analysis. Results Unified Theory of Acceptance and Use of Technology scores showed the majority of participants responding positively to the major constructs of Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions, with mean Likert scores (out of 7) of 6.41 (±1.43), 6.25 (±1.41), 5.44 (±1.43) and 5.32 (±1.99), respectively. There was also a young average user age (29.1 years). Qualitative analysis identified response themes of ease of use, efficiency and potential for future research and clinical use; users also suggested expansion of the type of platforms the application was available on. Conclusion Though a number of challenges exist for sustaining trauma registries in low- and middle-income countries, substantial involvement of local stakeholders and responsiveness to feedback should be used to facilitate the use of these technologies in developing countries. This study demonstrates a potential methodology for developing and evaluating trauma registry technologies for use in low- and middle-income countries.
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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 | 0.003 | 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 | 0.001 | 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