A Reflective Thematic Analysis Into the Perceptions of Pregnant Radiographers Regarding the Usefulness of the PregiDose Mobile App to Enhance Fetal Dosimetry and Well-Being: Qualitative Exploration
Bibliographic record
Abstract
BACKGROUND: Pregnancy apps are widely used by pregnant women, who benefit from self-tracking features to support their health goals. Pregnant radiographers are considered a high-risk group of health workers practicing in ionizing radiation environments. Radiation exposure above threshold limits can cause harmful genetic effects on a fetus. Accordingly, pregnant radiographers are required to wear special fetal dosimeters, which provide real-time readings of radiation dose exposure to the fetus. Pregnant radiographers have the responsibility to self-track their fetal doses to ensure that the threshold limit of 1 mGy is not exceeded. The traditional method used to track doses includes a written log of doses in a notebook. Thus, PregiDose, a unique offering in the context of pregnancy apps, was developed to enhance fetal dose tracking and monitoring using technological methods. OBJECTIVE: This study aims to describe the users' perceptions of the app's usefulness using PregiDose in a natural setting. METHODS: The overarching framework adopted for the study was a design science research (DSR) methodology encompassing five steps, namely (1) problem awareness, (2) suggestion, (3) development, (4) evaluation, and (5) conclusion. This paper presents the evaluation step of DSR. DSR step 4 included a qualitative approach to explore users' perceptions regarding the app. Data were collected using a semistructured interview guide. Open-ended questions were guided by the app's core features, namely dose tracking, education, and wellness. In total, 17 pregnant radiographers in South Africa enrolled to use the app, 9 (53%) engaged with the app, and 4 (24%) agreed to participate in the feedback interviews. The data were collected from October 2023 to March 2024 and analyzed using a reflective thematic data analysis method. RESULTS: Three overarching themes emerged from the data, namely (1) usefulness of PregiDose, (2) barriers to PregiDose adoption and use, and (3) recommendations for the advancement of PregiDose. Users labeled the app's usefulness as positive and perceived it as a modern approach to traditional dose-tracking methods. They perceived the graph output of the dose-tracking feature to be useful for viewing their accumulative doses. They did not fully engage with the journaling feature, indicating that it was a personal preference and not a practice they would usually engage in. Physiological barriers, such as fatigue and "pregnancy brain," were contributors to decreased engagement. Finally, because of the demanding workload and fast-paced nature of the radiography department, users recommended the automation of fetal dosimetry through the Internet of Things. CONCLUSIONS: PregiDose is an occupational health and safety mobile app developed for pregnant radiographers through a DSR approach. The app offers a modern method of dose tracking consistent with technological advancements in the context of self-tracking. However, future implementation would require using Internet of Things to make fetal dose tracking more effective.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".