Using Simulation to Enhance Primary Care Sexual Health Services for Breast Cancer Survivors: A Feasibility Study
Bibliographic record
Abstract
Abstract Purpose To evaluate the impact of a virtual simulation game (VSG) to improve primary care sexual health services for breast cancer survivors. Methods We developed a VSG to help primary care providers (PCPs) address sexual health disturbances among breast cancer survivors. We used a pretest–posttest design with a series of validated tools to assess the feasibility and perceived impact of the game, including an open-ended question about participants’ perceptions. Quantitative data was analyzed using descriptive and inferential statistics, and qualitative data through an inductive content analysis approach. Results Of the 60 participants, the majority were nurse practitioner students (n = 26; 43.3%); female (n = 48; 80%); and worked full-time (n = 35; 58.3%). Participants perceived the game as feasible and potentially effective. The intervention elicited an improvement in PCPs’ perception of knowledge between pretest and posttest surveys (z = -1.998, p = .046). Professional background and previous exposure to sexual health training were predictors of knowledge perception. Participants described the intervention as an engaging educational strategy where they felt safe to make mistakes and learn from that. Conclusions VSGs can be a potentially effective educational approach for PCPs. Our findings indicate that despite being an engaging interactive strategy, VSG interventions should be tailored for each professional group. Implications for cancer survivors This intervention has the potential to improve the knowledge and practice of PCPs related to breast cancer follow-up care to support comprehensive care for survivors, resulting in a better quality of life and patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".