This study aims to investigate international students’ perspectives on service quality and analyse factors contributing to the perceived service quality of the university health centre.

International students are at increased risk of experiencing poor mental health, isolation from families and cultures, language barriers, financial stress and academic pressures. It is important that universities support international students to enable them to complete their degrees and reach their desired level of achievement and performance. One of the supports required by international students is the availability of healthcare services. Therefore, improving the quality of services to meet international students’ requirements, including healthcare services, is essential.

A three-phase exploratory sequential mixed methods design was used. Phase 1 aims to explore international students’ perceptions of primary healthcare quality by conducting in-depth interviews and focus group discussions. Data were analysed using thematic analysis. Phase 2 is to form questionnaire items based on the results of the qualitative study. The questionnaire is subject to pilot testing to measure validity and reliability. Phase 3 analyses factors influencing international students’ perceived primary healthcare service quality. Multiple regression was used to analyse factors contributing to the perceived service quality of international students.

The qualitative strand revealed five major themes representing the study participants’ thoughts about the quality of services in the university healthcare context. Perceived quality attributes identified in this study were primarily empathy, equity, effectiveness, efficiency and safety. The quantitative strand found that 35.57% of participants consider the perceived quality of the centre as good. The highest and lowest service quality attributes were related to safety and efficiency, with a score of 21.12 ± 3.58 and 19.57 ± 4.34, respectively. The multiple linear regression analyses showed that PhD students from Health Faculty and Scholarship awardees were significantly associated with the perceived quality of healthcare services. Thus, the university management needs to improve service quality considering the diversity of international students’ socio-demographic characteristics.

Cardiac intensivists frequently assess patient readiness to wean off mechanical ventilation with an extubation readiness trial despite it being no more effective than clinician judgement alone. We evaluated the utility of high-frequency physiologic data and machine learning for improving the prediction of extubation failure in children with cardiovascular disease.

This was a retrospective analysis of clinical registry data and streamed physiologic extubation readiness trial data from one paediatric cardiac ICU (12/2016-3/2018). We analysed patients’ final extubation readiness trial. Machine learning methods (classification and regression tree, Boosting, Random Forest) were performed using clinical/demographic data, physiologic data, and both datasets. Extubation failure was defined as reintubation within 48 hrs. Classifier performance was assessed on prediction accuracy and area under the receiver operating characteristic curve.

Of 178 episodes, 11.2% (N = 20) failed extubation. Using clinical/demographic data, our machine learning methods identified variables such as age, weight, height, and ventilation duration as being important in predicting extubation failure. Best classifier performance with this data was Boosting (prediction accuracy: 0.88; area under the receiver operating characteristic curve: 0.74). Using physiologic data, our machine learning methods found oxygen saturation extremes and descriptors of dynamic compliance, central venous pressure, and heart/respiratory rate to be of importance. The best classifier in this setting was Random Forest (prediction accuracy: 0.89; area under the receiver operating characteristic curve: 0.75). Combining both datasets produced classifiers highlighting the importance of physiologic variables in determining extubation failure, though predictive performance was not improved.

Physiologic variables not routinely scrutinised during extubation readiness trials were identified as potential extubation failure predictors. Larger analyses are necessary to investigate whether these markers can improve clinical decision-making.

Patients’ experience of the quality of care received throughout their continuum of care can be used to direct quality improvement efforts in areas where they are most needed. This study aims to establish validity and reliability of the Healthcare Access and Patient Satisfaction Questionnaire (HAPSQ) – a tool that collects patients’ experience that quantifies aspect of care used to make judgments about quality from the perspective of the Alberta Quality Matrix for Health (AQMH).

The AQMH is a framework that can be used to assess and compare the quality of care in different healthcare settings. The AQMH provides a common language, understanding, and approach to assessing quality. The HAPSQ is one tool that is able to assess quality of care according to five of six AQMH’s dimensions.

This was a prospective methodologic study. Between March and October 2015, a convenience sample of patients presenting with chronic full-thickness rotator cuff tears was recruited prospectively from the University of Calgary Sport Medicine Centre in Calgary, Alberta, Canada. Reliability of the HAPSQ was assessed using test–retest reliability [interclass correlation coefficient (ICC)>0.70]. Validity was assessed through content validity (patient interviews, floor and ceiling effects), criterion validity (percent agreement >70%), and construct validity (hypothesis testing).

Reliability testing was completed on 70 patients; validity testing occurred on 96 patients. The mean duration of symptoms was three years (SD: 5.0, range: 0.1–29). Only out-of-pocket utilization possessed an ICC<0.70. Patients reported that items were relevant and appropriate to measuring quality of care. No floor or ceiling effects were present. Criterion validity was reached for all items assessed. A priori hypotheses were confirmed. The HAPSQ represents an inexpensive, reliable, and valid approach toward collecting clinical information across a patient’s continuum of care.