Health Care Professionals’ Perspectives on Technology Use in Urinary Care: Cross-Sectional Survey-Based Study
Bibliographic record
Abstract
BACKGROUND: Urinary issues pose a significant burden on health care systems, necessitating innovative solutions to enhance patient care and alleviate the provider burden. OBJECTIVE: The objective of this study was to explore health care professionals' perceptions of urinary issues and assess their acceptance and readiness to adopt wearable and remote technologies for managing these conditions. The study aimed to identify the attributes and barriers influencing technology integration in clinical practice, using established theoretical frameworks, such as the Health Belief Model (HBM) and the Technology Acceptance Model (TAM). METHODS: A cross-sectional survey-based study was conducted. A structured survey questionnaire was administered online to a sample of 256 health care professionals recruited through social media and personal networks. The survey included both closed- and open-ended questions to gather data. Quantitative data were analyzed using descriptive statistics, Pearson correlation, and multiple regression. RESULTS: Quantitative analysis revealed strong correlations between belief agreement and factors such as health literacy (r=0.591, P<.001), the perceived burden (r=0.628, P<.001), device attributes (r=0.650, P<.001), and support services (r=0.622, P<.001). Multiple regression analysis identified that the perceived burden (β=.284, P=.01), device attributes (β=.371, P<.001), and integrating technology (β=.312, P<.001) are positively associated. The survey demonstrated strong internal consistency, with Cronbach α=.85, indicating high reliability in measuring health care professionals' perceptions of technology adoption. CONCLUSIONS: Health care professionals' acceptance of technology in managing urinary issues is influenced by factors such as the perceived burden, device attributes, and the ease of integrating technology into existing workflows. Addressing barriers to technology adoption, providing comprehensive training and support, and prioritizing user-centered design are crucial for successful technology integration. Future research should focus on longitudinal studies and explore the perspectives of patients and other stakeholders to gain a more holistic understanding of technology integration in urological care.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".