Using QR Codes as a Form of eHealth to Promote Health Among Women in a Pandemic: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: QR codes have played an integral role during the pandemic in many sectors, but their use has been limited in the health care sector, especially by patients. Although some authors have stated that developing specific content for women on how to cope with health problems could be an effective way to prevent problems, especially during pandemics, there is little research regarding the use of QR codes to promote health during a pandemic, and even fewer studies are focused on women. Moreover, although the importance of assessing these interventions from the users' perspective has been stated, research carried out from this point of view is still scarce. OBJECTIVE: This study aimed to assess the usefulness of using QR codes with information to promote women's health in the context of a pandemic. We also sought to design and validate a questionnaire to assess this. METHODS: A cross-sectional study was conducted among women in the gynecology waiting rooms of a reference hospital. Exploratory factorial analysis with the split-half method and Cronbach α values was performed for questionnaire validation. Univariant and bivariant analyses were performed to analyze the data obtained. RESULTS: In total, 186 women took part in the study. Exploratory factor analysis identified 2 domains: usability and applicability in medical practice. The Cronbach α value was .81. Overall, 83.7% of the answers to the first domain and 56.4% of those to the second were favorable. Women with university education or those who had used QR codes before scored better in the usability domain, while no differences were observed in the applicability scores. CONCLUSIONS: Using QR codes in the gynecology clinics' waiting rooms can help promote women's health during a pandemic, regardless of their education level or whether they have used QR codes before. The questionnaire developed herein is a helpful tool to assess this. These findings are important for clinical practice. This research can be performed in other ambits, specialties, or countries.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".