Evaluating the Usability, Acceptability, User Experience, and Design of an Interactive Responsive Platform to Improve Perinatal Nurses’ Stigmatizing Attitudes Toward Substance Use in Pregnancy: Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Perinatal nurses are increasingly encountering patients who have engaged in perinatal substance use (PSU). Despite growing evidence demonstrating the need to reduce nurses' stigmatizing attitudes toward PSU, limited interventions are available to target these attitudes and support behavior change, especially those reflecting the overwhelming evidence that education alone is insufficient to change practice behavior. Arts-based interventions are associated with increasing nursing empathy, changing patient attitudes, improving reflective practice, and decreasing stigma. We adapted ArtSpective for PSU-a previously evaluated, in-person, arts-based intervention to reduce stigma toward PSU among perinatal nurses-into an interactive, digital, and responsive platform that facilitates intervention delivery asynchronously. OBJECTIVE: This study aimed to evaluate the usability, acceptability, and feasibility of the interactive, responsive platform version of ArtSpective for PSU. Our goal was to elicit the strengths and weaknesses of the responsive platform by evaluating the user experience to identify strategies to overcome them. METHODS: This study used a mixed methods approach to explore the platform's usability, user experience, and acceptability as an intervention to address stigma and implicit bias related to PSU. Theatre testing was used to qualitatively assess usability and acceptability perspectives with nurses and experts; a modified version of the previously validated 8-item Abbreviated Acceptability Rating Profile was used for quantitative assessment. Quantitative data for acceptability and satisfaction were analyzed using descriptive statistics. All qualitative data were analyzed iteratively using an inductive framework analysis approach. RESULTS: Overall, 21 nurses and 4 experts in stigma, implicit bias, and instructional design completed theatre-testing sessions. The mean duration of interviews was 31.92 (SD 11.32) minutes for nurses and 40.73 (SD 8.57) minutes for experts. All participants indicated that they found the digital adaptation of the intervention to be highly acceptable, with mean acceptability items ranging from 5.0 (SD 1.0) to 5.5 (SD 0.6) on a 1-6 agreement scale. Nurses reported high satisfaction with the platform, with mean satisfaction items ranging from 5.14 (SD 0.56) to 5.29 (SD 0.63) on a 1-6 agreement scale. In total, 1797 interview segments were coded from the theatre-testing sessions with 4 major themes: appearance, navigation, characterization, and overall platform, and 16 subthemes were identified. Consistent with the quantitative findings, the results were positive overall, with participants expressing high satisfaction related to the platform's appearance, the ease with which they could navigate the various modules, engagement, clarity of the presentation, and feasibility of being completed asynchronously. CONCLUSIONS: Developing and evaluating the usability of a digital adaptation of ArtSpective for PSU resulted in strong support for the usability, acceptability, and satisfaction of the program. It also provided insight into key aspects related to acceptability and usability that should be considered when designing a digital adaptation of an arts-based intervention for health care providers.
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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 | 0.036 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".