Technology-Related Trauma in Sexual and Reproductive Health Digital Technologies: Grounded Theory Study
Bibliographic record
Abstract
Background: Digital health technologies are increasingly used as complementary and alternative means of seeking sexual and reproductive health services. These platforms now play a critical role in facilitating services such as contraception counseling, abortion care, sexually transmitted infection testing and treatment, and fertility-related support, particularly for individuals who face barriers to in-person care. Despite their increasing prevalence, there is an emerging concern that such platforms could inadvertently trigger or perpetuate trauma among end-user patients. This risk is particularly salient for individuals from equity-deserving populations who already navigate stigma, discrimination, or prior traumatic experiences in health care settings. Objective: This study aimed to develop a theoretical account of how digital health technologies can cause or perpetuate emotional trauma among people who seek technology-based sexual and reproductive health services. Methods: We used the Charmaz constructivist grounded theory approach by conducting interviews with 25 participants who have used government and other regulated digital health platforms (ie, web-based platforms and mHealth apps) to access sexual and reproductive health information or services including sexually transmitted infection testing, contraception, and abortion. Data analysis occurred alongside data collection, and data were analyzed inductively using open, axial, and theoretical coding. Results: We developed an explanatory model that shows that technology-related harm can occur in two main ways: (1) digital platform design features (ie, navigation challenges, data and security breaches, and inappropriate display of content) and (2) digital platform-related interpersonal interactions (targeted campaigns and depersonalized digital health interactions). While these activities can cause harm to users in general, they are more likely to result in emotional trauma for individuals with prior traumatic experiences and in emotional discomfort for those without such histories. Conclusions: Web-based platforms provide opportunities for advancing access to sexual and reproductive health and services. At the same time, these technologies can also serve as conduits through which trauma can be triggered, perpetuated, and exacerbated. While technology-related trauma could occur unintentionally via design choices, some activities, including technology-related interactions, could trigger or perpetuate trauma among end users. To mitigate the risks, both technology developers (particularly designers) and health providers should consider design choices and implementation strategies that not only prevent trauma but also promote users' emotional well-being.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".