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Supplemental material, sj-pdf-1-inc-10.1177_1751143720954723 for At the end: A vignette-based investigation of strategies for managing end-of-life decisions in the intensive care unit by Tom W Reader, Ria Dayal and Stephen J Brett in Journal of the Intensive Care Society

Aim: Large databases of species records such as those generated through citizen science projects, archives or museum collections are being used with increasing frequency in species distribution modelling (SDM) for conservation and land management. Despite the broad spatial and temporal coverage of the data, its application is often limited by the issue of sampling bias and consequently, zero inflation; there are more zeros (which are potentially ‘false absences’) in the data than expected. Here, we demonstrate how pooling species presence data into a ‘pseudo-abundance’ count can allow identification and removal of sampling bias through the use of zero-inflated (ZI) models, and thus solve a common SDM problem. Location: All locations. Taxon: All taxa. Methods: We present the results of a series of simulations based on hypothetical ecological scenarios of data collection using random and non-random sampling strategies. Our simulations assume that the locations of occurrence records are known at a high spatial resolution, but that the absence of occurrence records may reflect under-sampling. To simulate pooling of presence–absence or presence-only data, we count occurrence records at intermediate and coarse spatial resolutions, and use ZI models to predict the counts (species abundance per grid cell) from environmental layers. Results: ZI models can successfully identify predictors of bias in species data and produce abundance prediction maps that are free from that bias. This phenomenon holds across multiple spatial scales, thereby presenting an advantage over presence-only SDM methods such as binomial GLMs or MaxEnt, where information about species density is lost, and model performance declines at coarser scales. Main Conclusions: Our results highlight the value of converting presence–absence or presence-only species data to ‘pseudo-abundance’ and using ZI models to address the problem of sampling bias. This method has huge potential for ecological researchers when using large species datasets for research and conservation.

Complaints are often seen as a negative experience in healthcare, with patients or family members writing about poor experiences of care, or things that went wrong. These things that go wrong may be small issues or major problems. Patient complaints are usually written with the aim of helping to improve healthcare for future patients. Patients may have a perspective that differs from healthcare providers. For example, providers might not know about how patients dignity was respected, whether they experienced delays, or how they were interacted with. Patients, however, know all of these things. It is known that patient insights into their care can improve healthcare. However, these insights may not be considered to the same extent as staff measures of quality and safety of care. Most assessments of quality of care in Irish healthcare services are focused on healthcare workers’ opinions, statistics about observable events (e.g., how many patients got an infection in hospital), or investigating large errors. Until recently, complaints have not been used by hospitals or the healthcare service in Ireland to make broad improvements to healthcare delivery. While these complaints receive individual responses, there is no focus on analysing these complaints together and using this data to learn about key issues in specific services and the healthcare system. This means that patients’ desire, and ability, to contribute to identifying problems and potential solutions are limited. Researchers at the London School of Economics have developed the Healthcare Complaints Analysis Tool (HCAT)1 that guides the systematic analysis of the cause and severity of complaints. The Health Research Board (HRB) and Health Service Executive (HSE) funded research project utilised the HCAT to analyse complaints received about Irish healthcare organisations. The research involved the collaboration of researchers, HSE managerial staff, healthcare workers, and quality and patient safety professionals in hospitals. An Applied Partnership Award funded by the Health Research Board and the HSE Quality Assurance and Verification Division (Grant number APA-2017-006) non-peer-reviewed

Ancient trees have important ecological, historical and social connections, and are a key source of dead and decaying wood, a globally declining resource. Wood-pastures, which combine livestock grazing, open spaces and scattered trees, are significant reservoirs of ancient trees, yet information about their true abundance within wood-pastures is limited. England has extensive databases of both ancient trees and wood-pasture habitat, providing a unique opportunity for the first large-scale, national case study to address this knowledge gap. We investigated the relationship between the abundance of ancient trees in a large sample of English wood-pastures (5,571) and various unique environmental, historical and anthropogenic predictors, to identify wood-pastures with high numbers of undiscovered ancient trees. A major challenge in many modelling studies is obtaining independent data for model verification: here we introduce a novel model verification step using series of historic maps with detailed records of trees to validate our model predictions. This desk-based method enables rapid verification of model predictions using completely independent data across a large geographical area, without the need for, or limitations associated with, extensive field surveys. Historic map verification estimates correlated well with model predictions of tree abundance. Model predictions suggest there are ~101,400 undiscovered ancient trees in all wood-pastures in England, around 10 times the total current number of ancient tree records. Important predictors of ancient tree abundance included wood-pasture area, distance to several features including cities, commons, historic Royal forests and Tudor deer parks, and different types of soil and land classes. Synthesis and applications. Historical maps and statistical models can be used in combination to produce accurate predictions of ancient tree abundance in wood-pastures, and inform future targeted surveys of wood-pasture habitat, with a focus on those deemed to have undiscovered ancient trees. This study provides support for improvements to conservation policy and protection measures for ancient trees and wood-pastures.

Determining the life history consequences for fishes living in extreme and variable environments will be vital in predicting the likely impacts of ongoing climate change on reef fish demography. Here, we compare size-at-age and maximum body size of two common reef fish species (Lutjanus ehrenbergii and Pomacanthus maculosus) between the environmentally extreme Arabian/Persian Gulf (‘Arabian Gulf’) and adjacent comparably benign Oman Sea. Additionally, we use otolith increment width profiles to investigate the influence of temperature, salinity and productivity on the individual growth rates. Individuals of both species showed smaller size-at-age and lower maximum size in the Arabian Gulf compared to conspecifics in the less extreme and less variable environment of the Oman Sea, suggesting a life-history trade-off between size and metabolic demands. Salinity was the best environmental predictor of interannual growth across species and regions, with low growth corresponding to more saline conditions. However, salinity had a weaker negative effect on interannual growth of fishes in the Arabian Gulf than in the Oman Sea, indicating Arabian Gulf populations may be better able to acclimate to changing environmental conditions. Temperature had a weak positive effect on the interannual growth of fishes in the Arabian Gulf, suggesting that these populations may still be living within their thermal windows. Our results highlight the potential importance of osmoregulatory cost in impacting growth, and the need to consider the effect of multiple stressors when investigating the consequences of future climate change on fish demography.

Supplemental material, sj-pdf-2-hsr-10.1177_13558196211029323 for Getting the whole story: Integrating patient complaints and staff reports of unsafe care by Jackie Van Dael, Alex Gillespie, Tom Reader, Katelyn Smalley, Dimitri Papadimitriou, Ben Glampson, Daniel Marshall and Erik Mayer in Journal of Health Services Research & Policy

General practice is a vital aspect of the health service, with large numbers of patient contacts every year1. While general practice is often seen as inherently less risky than secondary care2, errors have been found to occur in between 2–3% of consultations3. A recent study in Irish out-of-hours general practice found a complaints rate of 0.61 per 1000 consultations4. Patient complaints are an under-utilised source of data about quality of care. Typically, complaints are resolved on an individual basis by responding to the person who made the complaint and addressing their concerns.It is acknowledged that receiving a complaint can be a stressful, negative experience for healthcare providers5. It may be beneficial for a healthcare provider who has received a conplaint against them to liase with an indeminfier, who will be in a position to offer help, support, amd reassurance. However, research and policy is shifting towards reframing complaints from a negative experience, to using them as a valuable source of data to support quality improvement efforts. Until recently there has been a lack of valid and reliable systems for examing trends across complaints at a regional and/or national level6, but there are now valid and reliable tools for analysing complaints about hospital and general practice care.The Healthcare Complaints Analysis Tool (HCAT) was developed to systematically analyse complaints about secondary care. Information on the HCAT is available online7. However, while the HCAT is a suitable tool for analysing hospital complaints, it cannot be directly applied to analysing complaints about general practice. This guide outlines how to use the Healthcare Complaints Analysis Tool (General Practice) [HCAT(GP)] to analyse general practice complaints. The HCAT(GP) is based upon the original HCAT, but tailored specifically for general practice. Evaluations of HCAT(GP) have found that it is a valid and reliable tool for analysing complaints about general practice. non-peer-reviewed

Supplemental material, sj-pdf-1-hsr-10.1177_13558196211029323 for Getting the whole story: Integrating patient complaints and staff reports of unsafe care by Jackie Van Dael, Alex Gillespie, Tom Reader, Katelyn Smalley, Dimitri Papadimitriou, Ben Glampson, Daniel Marshall and Erik Mayer in Journal of Health Services Research & Policy

Additional file 1. Interview guide and vignettes.