Background: Because of the global spread of coronavirus disease 2019 (COVID‐19), oncology departments across the world have rapidly adapted their cancer care protocols to balance the risk of delaying cancer treatments and the risk of COVID‐19 exposure. COVID‐19 and associated changes may have an impact on the psychosocial functioning of patients with cancer and survivors. This study was designed to determine the impact of the COVID‐19 pandemic on young people living with and beyond cancer.Methods: In this cross‐sectional study, 177 individuals, aged 18 to 39 years, were surveyed about the impact of COVID‐19 on their cancer care and psychological well‐being. Participants also reported their information needs with respect to COVID‐19. Responses were summarized with a content analysis approach.Results: This was the first study to examine the psychological functioning of young patients and survivors during the first weeks of the COVID‐19 pandemic. A third of the respondents reported increased levels of psychological distress, and as many as 60% reported feeling more anxious than they did before COVID‐19. More than half also wanted more information tailored to them as young patients with cancer.Conclusions: The COVID‐19 pandemic is rapidly evolving and changing the landscape of cancer care. Young people living with cancer are a unique population and might be more vulnerable during this time in comparison with their healthy peers. There is a need to screen for psychological distress and attend to young people whose cancer care has been delayed. As the lockdown begins to ease, the guidelines about cancer care should be updated according to this population's needs.

Background: Adolescent cancer survivors are a particularly vulnerable group of young patients. Compared with healthy peers, adolescent survivors face more psychosocial difficulties and are at increased risk of developing psychiatric disorders.Aims: We aimed to establish prevalence rates and predictors of psychiatric disorders in young cancer survivors and discuss areas for targeted interventions.Method: We systematically reviewed four major online databases: Embase, PsychINFO, Scopus, and Medline for quantitative studies evaluating mental health in adolescent cancer survivors. We used a narrative synthesis approach.Results: Nineteen studies met our inclusion criteria. Across the sample, up to 34% met criteria for post‐traumatic stress disorder (PTSD), 13% for clinical depression, and 8% for anxiety. Maladaptive coping, illness relapse, higher number of late effects, brain tumor diagnosis, and poor family functioning and parental distress were associated with higher psychological distress.Conclusions: A significant subset of adolescent survivors reports PTSD, anxiety, and depression symptoms. Individuals who present with more vulnerabilities and higher risk indices should be routinely assessed in order to reduce the psychological, social, and economic burden associated with poor mental health in this population. Early prevention strategies should target maladaptive coping mechanisms and promote healthy peer relationships and family functioning.

Latent change score models (LCSMs) are used across disciplines in behavioural sciences to study how constructs change over time. LCSMs can be used to estimate the trajectory of one construct (univariate) and allow the investigation of how changes between two constructs (bivariate) are associated with each other over time. This paper introduces the R package lcsm, a tool that aims to help users understand, analyse, and visualise different latent change score models. The lcsm package provides functions to generate model syntax for basic univariate and bivariate latent change score models with different model specifications. It is also possible to visualise different model specifications in simplified path diagrams. An interactive application illustrates the main functions of the package and demonstrates how the model syntax and path diagrams change based on different model specifications. This R package aims to increase the transparency of reporting analyses and to provide an additional resource to learn latent change score modelling.

COVID-19 research has relied heavily on convenience-based samples, which—though often necessary—are susceptible to important sampling biases. We begin with a theoretical overview and introduction to the dynamics that underlie sampling bias. We then empirically examine sampling bias in online COVID-19 surveys and evaluate the degree to which common statistical adjustments for demographic covariates successfully attenuate such bias. This registered study analysed responses to identical questions from three convenience and three largely representative samples (total N = 13,731) collected online in Canada within the International COVID-19 Awareness and Responses Evaluation Study (www.icarestudy.com). We compared samples on 11 behavioural and psychological outcomes (e.g., adherence to COVID-19 prevention measures, vaccine intentions) across three time points and employed multiverse-style analyses to examine how 512 combinations of demographic covariates (e.g., sex, age, education, income, ethnicity) impacted sampling discrepancies on these outcomes. Significant discrepancies emerged between samples on 73% of outcomes. Participants in the convenience samples held more positive thoughts towards and engaged in more COVID-19 prevention behaviours. Covariates attenuated sampling differences in only 55% of cases and increased differences in 45%. No covariate performed reliably well. Our results suggest that online convenience samples may display more positive dispositions towards COVID-19 prevention behaviours being studied than would samples drawn using more representative means. Adjusting results for demographic covariates frequently increased rather than decreased bias, suggesting that researchers should be cautious when interpreting adjusted findings. Using multiverse-style analyses as extended sensitivity analyses is recommended.