Use of Health Apps by Nurses for Professional Purposes: Web-Based Survey Study
Bibliographic record
Abstract
BACKGROUND: In the last few years, the number of mobile apps for health professionals has increased exponentially. Nevertheless, there is a lack of knowledge about the professional use, training requirements, and quality perception of these apps among health care professionals such as nurses. Considering that the nursing profession is the largest segment of health care workforce in many countries such as Spain, the impact of the use of health apps by these professionals can be critical to the future of modern health care. OBJECTIVE: The main objective of this study was to determine if nurses were using health apps professionally and what types of apps they were using. The secondary objectives were (1) to find out if, among nurses, there is a need for training in the use of health apps and (2) to explore nurses' perceptions of health professional apps, determining whether there is a need for a certification process for health apps and the type of institution or organization that should review and validate these apps for professional use. METHODS: After an initial piloting survey, all registered nurses at the Nursing Association of Barcelona were invited to participate in a 34-item online survey. Eventually, 1293 nurses participated in the survey; however, 52 did not complete the survey properly, omitting both age or gender information, and they were excluded from the analysis. RESULTS: About half of the respondents (600/1241, 48.35%) had health professional apps installed on their devices and were included for analysis. Most participants in the survey were women (474/600, 79.0%) and the remaining were men (126/600, 21.0%). The most popular types of apps used and installed among nurses were related to drug information, health calculators, and health guidelines. Overall, 97.0% (582/600) of nurses thought that the health apps should be certified, and 80.0% (480/600) agreed that the certification process should be carried out by professional or health institutions. Furthermore, 14.5% (87/600) of participants mentioned that they were asked by their patients to prescribe a health app and only 6.5% (28/430) recommended them often. Most nurses (354/433, 81.8%) who answered the question about the importance of receiving specific training on using and prescribing health apps considered this point a very relevant issue. CONCLUSIONS: About half of the nurses in Catalonia use health apps for professional purposes, and they believe that these types of tools should be validated and certified by health or professional institutions before using them in clinical environments. Although the prescription of health apps in clinical environments is infrequent among nurses, they would be willing to prescribe apps if they were certified by a health organization. Finally, among nurses, there is a need for training in using and prescribing health apps for health care purposes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".