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Abstract Background To maintain and improve the quality of the cancer nursing workforce, it is crucial to understand the factors that influence retention and job satisfaction. We aimed to investigate the characteristics of cancer nurses in Australia and identify predictors of job satisfaction. Methods We analysed data from an anonymous cross-sectional survey distributed through the Cancer Nurses Society Australia membership and social media platforms from October 2021 to February 2022. The survey was compared to national nursing registration data. Data were analysed with non-parametric tests, and a stepwise, linear regression model was developed to best predict job satisfaction. Results Responses were received from 930 cancer nurses. Most respondents (85%) described themselves as experienced nurses, and more than half had post-graduate qualifications. We identified individual, organizational, and systemic factors that contribute to job satisfaction and can impact in workforce shortages. The findings include strategies to address and prioritize workforce challenges. There were 89 different titles for advanced practice nursing roles. Managing high workload was a reported challenge by 88%. Intention to stay less than 10 years was reported by nearly 60%; this was significantly correlated with job satisfaction and age. Significantly higher scores for job satisfaction were associated with those who had career progression opportunities, career development opportunities, adequate peer support and a clearly defined scope of role. Conversely, job satisfaction scores decreased the more people agreed there was a lack of leadership and they had insufficient resources to provide quality care. Conclusion Cancer nurses are critical to the delivery of cancer care however, the workforce faces multiple challenges. This study provides an understanding of the Australian cancer nursing workforce characteristics, their roles and activities, and highlights important considerations for retaining nurses in the profession.

Abstract Background In disease areas with ‘soft’ outcomes (i.e., the subjective aspects of a medical condition or its management) such as migraine or depression, extraction and validation of real-world evidence (RWE) from electronic health records (EHRs) and other routinely collected data can be challenging due to how the data are collected and recorded. In this study, we aimed to define and validate a scalable framework model to measure outcomes of migraine treatment and prevention by use of artificial intelligence (AI) algorithms within EHR data. Methods Headache specialists defined descriptive features based on routinely collected clinical data. Data elements were weighted to define a 10-point scale encompassing headache severity (1–7 points) and associated features (0–3 points). A test data set was identified, and a reference standard was manually produced by trained annotators. Automation (i.e., AI) was used to extract features from the unstructured data of patient encounters and compared to the reference standard. A threshold of 70% close agreement (within 1 point) between the automated score and the human annotator was considered to be a sufficient extraction accuracy. The accuracy of AI in identifying features used to construct the outcome model was also evaluated and success was defined as achieving an F1 score (i.e., the weighted harmonic mean of the precision and recall) of 80% in identifying encounters. Results Using data from 2,006 encounters, 11 features were identified and included in the model; the average F1 scores for automated extraction were 92.0% for AI applied to unstructured data. The outcome model had excellent accuracy in characterizing migraine status with an exact match for 77.2% of encounters and a close match (within 1 point) for 82.2%, compared with manual extraction scores—well above the 70% match threshold set prior to the study. Conclusion Our findings indicate the feasibility of technology-enabled models for validated determination of soft outcomes such as migraine progression using the data elements typically captured in the real-world clinical setting, providing a scalable approach to credible EHR-based clinical studies. Graphical Abstract

Abstract Background Intense and aggressive treatment regimens for most children’s cancer have achieved vast improvements in survival but are also responsible for both a high number and burden of symptoms. The use of Patient Reported Outcome Measures (PROMs) demonstrates a range of benefits for improved symptom management in adults with cancer. There are, however, multiple barriers to integrating PROMs into routine care in children and adolescents with cancer. This study aims to evaluate: (1) the effectiveness of electronic PROMs to generate stratified alerts, symptom management recommendations and graphical summaries (the RESPONSE system) to improve health outcomes and (2) the implementation of the RESPONSE system by assessing feasibility, acceptability, satisfaction, and sustainability. Methods A pragmatic hybrid II effectiveness-implementation controlled trial, using mixed methods, will be undertaken, advancing both knowledge of the effectiveness of the intervention and implementation factors. One-hundred and sixty children with cancer receiving active treatment will be recruited 1:1 to a non-randomised study involving two groups with an equal number of participants in each group. The intervention group (n = 80) will be prospectively recruited to receive the RESPONSE system intervention over eight weeks, versus the historical matched control group (n = 80) who will complete the ePROMs without access to the RESPONSE system. The primary outcome of the effectiveness trial is change between groups in total symptom burden. Secondary outcomes include child health-related quality-of-life and implementation outcomes. Trial data will be analysed using linear mixed-effects models. Formative implementation evaluation is informed by CFIR and ERIC frameworks and implementation outcomes will be mapped to the RE-AIM framework and include interviews, field notes, as well as administrative data to evaluate feasibility, acceptability, satisfaction and sustainability. Trial registration number ACTRN12621001084875 . Retrospectively Registered 16 August 2021.

Abstract Background Phenylalanine hydroxylase (PAH) deficiency is an autosomal recessive disorder that results in elevated concentrations of phenylalanine (Phe) in the blood. If left untreated, the accumulation of Phe can result in profound neurocognitive disability. The objective of this systematic literature review and meta-analysis was to estimate the global birth prevalence of PAH deficiency from newborn screening studies and to estimate regional differences, overall and for various clinically relevant Phe cutoff values used in confirmatory testing. Methods The protocol for this literature review was registered with PROSPERO (International prospective register of systematic reviews). Pubmed and Embase database searches were used to identify studies that reported the birth prevalence of PAH deficiency. Only studies including numeric birth prevalence reports of confirmed PAH deficiency were included. Results From the 85 publications included in the review, 238 birth prevalence estimates were extracted. After excluding prevalence estimates that did not meet quality assessment criteria or because of temporal and regional overlap, estimates from 45 publications were included in the meta-analysis. The global birth prevalence of PAH deficiency, estimated by weighting regional birth prevalences relative to their share of the population of all regions included in the study, was 0.64 (95% confidence interval [CI] 0.53–0.75) per 10,000 births and ranged from 0.03 (95% CI 0.02–0.05) per 10,000 births in Southeast Asia to 1.18 (95% CI 0.64–1.87) per 10,000 births in the Middle East/North Africa. Regionally weighted global birth prevalences per 10,000 births by confirmatory test Phe cutoff values were 0.96 (95% CI 0.50–1.42) for the Phe cutoff value of 360 ± 100 µmol/L; 0.50 (95% CI 0.37–0.64) for the Phe cutoff value of 600 ± 100 µmol/L; and 0.30 (95% CI 0.20–0.40) for the Phe cutoff value of 1200 ± 200 µmol/L. Conclusions Substantial regional variation in the birth prevalence of PAH deficiency was observed in this systematic literature review and meta-analysis of published evidence from newborn screening. The precision of the prevalence estimates is limited by relatively small sample sizes, despite widespread and longstanding newborn screening in much of the world.