Defining destigmatizing design guidelines for use in sexual health-related digital technologies: A Delphi study
Bibliographic record
Abstract
OBJECTIVE: Stigma has been recognized as a significant issue in sexual health, yet no specific guidelines exist to support digital health development teams in creating stigma-alleviating sexual health digital platforms. The purpose of this study was to develop a set of design guidelines that would serve as a reference point for addressing stigma during the design of sexual health-related digital platforms. MATERIALS AND METHODS: We conducted a 3-round Delphi study among 14 researchers in stigma and sexual health. A preliminary list of 28 design guidelines was generated from a literature review. Participants appraised and critiqued the clarity and usefulness of the preliminary list and provided comments for each item and for the overall group of items at each round. At each round, a content validity index and an interquartile range were calculated to determine the level of consensus regarding the clarity and usefulness of each guideline. Items were retained if there was high consensus or were dropped if there was no consensus after the three rounds. RESULTS: Nineteen design guidelines achieved consensus. Most of them were content-related guidelines and sought to address the emotional concerns of patients that could potentially aggravate stigma. The findings also reflected modern stigma management strategies of making stigma a societal attribute by challenging, exposing, and normalizing stigma attributes via web platforms. CONCLUSION: To address stigma via digital platforms, developers should not just concentrate on technical solutions but seriously consider content-related and emotional design components that are likely to result in stigma.
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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 | 0.234 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".