Health care needs, eHealth literacy, use of mobile phone functionalities, and intention to use it for self-management purposes by informal caregivers of children with burns: a survey study
Bibliographic record
Abstract
BACKGROUND: This study aimed to assess health care needs, electronic health literacy, mobile phone usage, and intention to use it for self-management purposes by informal caregivers of children with burn injuries. METHODS: This cross-sectional research was carried out in 2021 with 112 informal caregivers of children with burns in a burn center in the north of Iran. The data collection tools were questionnaires that included the participants' demographics, their E-Health Literacy, their current mobile phone usage, and their desires for mobile phone use for burn care services. RESULTS: Most informal caregivers had smartphones (83.0%) and Internet access (81.3%). Most participants occasionally used phone calls (63.4%), the Internet (45.5%), and social media (42.9) to receive information about psychosocial disorders, infection control, wound care, pain, itch, physical exercise, and feeding. Most participants have never used some of the mobile phone functionalities to receive burn-related information, such as applications/Software (99.1%) and e-mail (99.1%). Nevertheless, most informal caregivers desire to use mobile applications for self-management purposes in the future (88.4%). The mean eHealth literacy score was 25.01 (SD = 9.61). Informal caregivers who had higher education levels, access to the Internet, and lived in urban areas had higher eHealth literacy (P < 001). CONCLUSION: The current research delivers beneficial information about the healthcare needs of informal caregivers and their preference to use mobile functionality to receive burns-related healthcare and rehabilitation information post-discharge. This information can help design and implement mobile health (mHealth) interventions to enhance the self-care skills of informal caregivers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 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".