Background: Waiting-room time is an important and neglected element of the outpatient experience in the field of oncology, which carries far-reaching consequences for overall patient satisfaction, perceptions of quality, and healthcare experience. Methods: We carried out a cross-sectional, mixed-methods study, conducted at the Juravinski Cancer Centre in Hamilton, Canada, from October to December 2024. Results: We studied 418 patients with a mean age of 61.6 years. Overall, 59.6% of respondents had spent less than 30 min in the waiting room, while 56.2% accepted a delay of 15–30 min. Waiting for more than 30–45 min was perceived as long by 61% of participants. In regards to physician choice, 59.8% of respondents preferred to be seen by their known provider, while 30.9% were ready to meet another one, particularly in terms of scheduled regular follow-up and urgent appointments followed by consultations. Interestingly, chemotherapy visits were the least common type of appointment where patients agreed to see different providers. A significant association was identified between appointment types and readiness to change physicians (p < 0.05). Although 78.7% agreed that delays were caused by complex conditions, only 29.2% received sufficient information about delays. Conclusions: Most gynecologic oncology patients want continuity of care, yet many are flexible with consultations, follow-up, and urgent visits. In outpatient clinics, patient-centered scheduling, balancing efficiency and patient satisfaction with better communication is an objective for system improvements.

The coefficient of variation (CV) is a frequently used standardized measure of variability that allows meaningful comparison among datasets measured using different scales or units. Although widely applied in practice, formal statistical inference for the CV under non-normal distributions has received limited attention. This study focuses on estimating the CV for the weighted exponential distribution under an adaptive Type-II hybrid progressive censoring scheme, which enhances the efficiency of life-testing experiments by balancing test duration and the number of observed failures. Both Bayesian and non-Bayesian frameworks are considered. Point estimation in the non-Bayesian framework is carried out using the maximum likelihood method, whereas interval estimates are constructed through the parametric bootstrap approach. For the Bayesian approach, posterior inference is performed through Markov chain Monte Carlo sampling with under appropriate gamma prior assumptions. To illustrate the implementation of the proposed procedures, a simulated dataset is analyzed, and a comprehensive Monte Carlo simulation study is subsequently conducted to evaluate the accuracy and efficiency of the estimators. According to the analysis, Bayesian estimators based on informative prior distributions yield more precise estimates of the CV.