طراحی و ارزیابی برنامه کاربردی آموزشی مبتنی بر گوشی های هوشمند برای مراقبان کودکان مبتلا به فلج مغزی
Bibliographic record
Abstract
Abstract Background : Nowadays, caregivers’ general health has an influential impact on patients’ and attendants’ education as well as the management of chronic diseases. This study aimed to design, develop and evaluate an Android application based on a user-led approach to educate caregivers of children with cerebral palsy. Methods: To conduct this applied developmental study, we first assessed the educational needs of caregivers using the standard Canadian Occupational Performance Measure questionnaire. Seven caregivers participated in this phase. Based on the known training needs, we developed and evaluated a prototype in several iterations with the participation of these seven caregivers. For final evaluation, 17 new caregivers participated. A validated researcher-designed questionnaire was used in order to evaluate its application. For data analysis, descriptive statistics using SPSS software version 16 was employed. Results : User’s feedback during designation of the application resulted in some changes. The final application involved 7 educational modules (including knowing about cerebral palsy, movement principles, nutrition, WC, bath, transportation, and game) and a drug reminder module. All of the participants (100%) were satisfied with the graphical features, the appearance and the content of the application. They (100%) expressed that the application is easy to use. More than 82 percent of the users believed that the application could meet their information needs for caring their children. In addition, over 75 percent of caregivers stated that it has been highly practical and that they had employed it in their everyday life care. Conclusion : It seems that this application can help caregivers of children with cerebral palsy to access the information required to provide daily care for their child at anytime and anywhere. Keywords : Smartphone, Cerebral Palsy, Education, Caregivers
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.020 |
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 source (direct Gemma or distilled Codex), 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".