Wound Care Knowledge, Attitudes, and Practices and Mobile Health Technology Use in the Home Environment: Cross-Sectional Survey of Social Network Users
Bibliographic record
Abstract
BACKGROUND: Injury causing wounds is a frequent event. Inadequate or inappropriate treatment of injuries can threaten individual health. However, little is known about wound care knowledge, attitudes, and practices and mobile health (mHealth) use in the home environment in Taiwan. OBJECTIVE: This study aimed to evaluate wound care knowledge, attitudes, and practices and mHealth technology use among social network users. METHODS: A cross-sectional survey on social media platforms was conducted on adults aged 20 years and older. Data were collected from social network users in the home environment. RESULTS: A total of 361 participants were enrolled. The mHealth technology use of participants was positively correlated with wound care knowledge (r=.132, P=.01), attitudes (r=.239, P<.001), and practices (r=.132, P=.01). Participants did not have adequate knowledge (correct rate 69.1%) and were unfamiliar with the guidelines of proper wound care (correct rate 74.5%). Most participants had positive attitudes toward wound care and mHealth technology use. A total of 95.6% (345/361) of participants perceived that the use of mHealth technology can improve wound care outcomes, and 93.9% (339/361) perceived that wound care products should be optimized to be used with a mobile device. However, 93.6% (338/361) of participants had no experience using mHealth technology for wound care. CONCLUSIONS: Our study shows the potential of mHealth technology to enhance wound care knowledge among social network users. Thus, government agencies and medical institutions in Taiwan should provide easy-to-use information products that enhance wound care knowledge, promote adequate behavior toward wound care, and prevent unpredictable or undesirable outcomes.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".