Update supplementary materials with new screenshots and descriptions for web scraping and evaluation interfaces
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src/index.qmd
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@@ -105,19 +105,19 @@ Grok 3 assisted in drafting manuscript sections. AI-generated content was review
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## Supplementary Materials
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- **Figures**:
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Evaluation interface configuration - Interface for configuring evaluation parameters with general criteria (title, description) and quality criteria (preventive or clinical, effectiveness) selection options. The pipeline shows running status with selected criteria for a digital health application assessment.
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Scraping and NLP processing results - Output display showing scraped data from 16 pages on the left panel with JSON structure containing URLs and page titles, and NLP analysis results on the right panel displaying extracted German text about a digital health application's purpose and effectiveness.
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Web scraping configuration panel - Configuration interface for web scraping parameters including depth limit slider (set to 2), page count selector (set to 10), URL input field populated with a GitHub Pages URL, and control buttons for running and monitoring scraping status.
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- **Repository Link**: [huggingface.co/DSSG-AmaMind](https://huggingface.co/DSSG-AmaMind)
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## References
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* Torous J
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* Wang K
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## Supplementary Materials
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- **Figures**:
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Web scraping configuration panel - Configuration interface for web scraping parameters including depth limit slider (set to 2), page count selector (set to 10), URL input field populated with a GitHub Pages URL, and control buttons for running and monitoring scraping status.
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Evaluation interface configuration - Interface for configuring evaluation parameters with general criteria (title, description) and quality criteria (preventive or clinical, effectiveness) selection options. The pipeline shows running status with selected criteria for a digital health application assessment.
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Scraping and NLP processing results - Output display showing scraped data from 16 pages on the left panel with JSON structure containing URLs and page titles, and NLP analysis results on the right panel displaying extracted German text about a digital health application's purpose and effectiveness.
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- **Repository Link**: [huggingface.co/DSSG-AmaMind](https://huggingface.co/DSSG-AmaMind)
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## References
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* Torous, J., Andersson, G., Bertagnoli, A., Christensen, H., Cuijpers, P., Firth, J., Haim, A., Hsin, H., Hollis, C., Lewis, S., Mohr, D. C., Pratap, A., Roux, S., Sherrill, J., & Arean, P. A. (2019). Towards a consensus around standards for smartphone apps and digital mental health. World psychiatry : official journal of the World Psychiatric Association (WPA), 18(1), 97–98. https://doi.org/10.1002/wps.20592
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* Wang, K., Varma, D. S., & Prosperi, M. (2018). A systematic review of the effectiveness of mobile apps for monitoring and management of mental health symptoms or disorders. Journal of psychiatric research, 107, 73–78. https://doi.org/10.1016/j.jpsychires.2018.10.006
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