“I Feel like the Virtual Reality Really Gives You that Power Back”: A Focus Group Study Exploring Breast Cancer Surgery Patients’ Experiences with a Preoperative Virtual Reality Intervention
Bibliographic record
Abstract
Patients undergoing oncological surgery often experience elevated anxiety, partly due to the novel hospital environments (e.g., operating room [OR]). Virtual reality (VR) may be a practical and immersive way to expose patients to such triggering environments before surgery to address this anxiety. However, patient perspectives are often underrepresented in the development of these interventions, and patient feedback is essential to ensure their relevance and utility. A randomized clinical feasibility trial investigated the use of a preoperative VR OR prototype intervention with breast cancer patients undergoing surgery. Participants who indicated interest in participating in related studies were invited to take part in either an in-person (for treatment as usual [TAU] to trial the VR OR postsurgery) or a virtual (previously received preoperative VR intervention) focus group. These were audiorecorded, transcribed, and analyzed using reflexive thematic analysis. The primary objectives were to understand patients' experiences with the VR intervention and gain patient feedback for future VR and study development. Ten participants from the feasibility study participated in one of four focus groups, six from the intervention group and four from the TAU group. Themes identified across focus groups included: (1) individuality shapes the VR experience, (2) wanting to know what to expect, (3) "a little more in charge of what's going on," and (4) "perspective of being on the (operating) table." The VR intervention was generally viewed positively and seen as a valuable resource. The focus groups were useful in deciphering patient suitability through discussion of individual differences and highlighting the benefits of the intervention, such as education, exposure to the OR, and facilitating a sense of autonomy and empowerment. While participants endorsed the VR intervention's current utility, they also provided meaningful suggestions for enhancement based on their lived experience. As new technologies such as VR are developed, it is essential to incorporate patient perspectives to ensure that these innovations effectively meet patient needs.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".