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Skip search results from other journals and go to results- 805 Journal of Medical Internet Research
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The software SPSS version 26.0 (IBM Corporation) was used for analysis, the measurement of normal distribution data were described by means and SDs, and 2-tailed independent sample t tests were used for comparison between groups. The count data were represented by the use case (%), and a chi-square test was used for the comparison between groups. The test level was bilateral (α=.05).
Participants (referred to as as P1, P2, and so on, up to P15) were selected using purposive and snowball sampling methods.
JMIR Med Inform 2025;13:e65445
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Data analysis used the χ2 test, Welch t test, binary logistic regression analysis, and multiple linear regression analysis, to compare the PPT and CRT between the intervention and control groups.
Ethical approval for this study was obtained from the Research and Ethics Committee at Zhejiang University Affiliated Sir Run Run Shaw Hospital (registry 2023-850-01).
JMIR Form Res 2025;9:e65617
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Mathematically, the forecast y^t+1:t+H is obtained by summing the outputs of each stack:
where Fi represents the forward projection function of the i-th stack,
and xt−L+1:t denotes the historical input sequence of length L.
The TFT model was used to predict ILI positive rates, using its hybrid structure that merges recurrent neural networks with attention mechanisms to grasp both short-term and long-term patterns.
J Med Internet Res 2025;27:e74423
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Integrating Yang and Chu’s [28] digital literacy framework with the media intervention model of Chen et al [38], we pioneer evidence of their mediating role in holistic digital inclusion. Digital engagement enhances psychological resilience through three pathways: remodeling social interactions, optimizing learning strategies, and diversifying recreational activities.
J Med Internet Res 2025;27:e71441
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The architecture integrates a simplified U-Net model, denoted as ∈prior (It, t, E), where It represents the noisy CLIP embedding at a specific diffusion step t. The classifier-free guidance method refined the diffusion model by conditioning it on a specific EEG input E. This approach synchronized the outputs of both the conditional and unconditional models. The final model equation was expressed as follows: . (3).
JMIR Med Inform 2025;13:e72027
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