Evaluating the adoption of a mobile application for quality-of-care assessments in South Sudan using Rogers’ innovation diffusion theory
Bibliographic record
Abstract
Introduction: The Health Pooled Fund (HPF) in South Sudan introduced the HPF Quality-of-Care (QoC) Application (App) in 2019 to improve healthcare quality monitoring and evaluation. The App allowed direct data entry at health facilities (HFs) and provided cloud storage for remote access and analysis. The App adoption can be understood through Everett Rogers’ Innovations Diffusion Theory (IDT), which explains how new ideas and technologies spread through societies. This study evaluated the QoC App adoption in South Sudan, using Rogers’ IDT to understand the factors influencing adoption, and identify successes and challenges in low-resource healthcare settings. Method: This study analysed QoC assessment data from HPF-supported HFs from 2019 to 2021, using descriptive statistics and thematic analysis to identify the trends and factors influencing adoption, based on Rogers’ IDT. Results: The study found that QoC App adoption significantly increased the proportion of HFs assessed from 39% in the first quarter when it was introduced, to 92.2% seven quarters later. The adoption of this innovation aligned with Rogers’ IDT. Conclusion: The successful implementation of the HPF QoC App demonstrates the practical application of Rogers’ IDT in a low-resource healthcare setting. The effective use of this App in South Sudan’s healthcare system has demonstrated digital health potential for future public health innovations and technology adoption process.
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 | 0.027 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".