To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Introduction: Crowding is associated with poor patient outcomes in emergency departments (ED). Measures of crowding are often complex and resource-intensive to score and use in real-time. We evaluated single easily obtained variables to establish the presence of crowding compared to more complex crowding scores. Methods: Serial observations of patient flow were recorded in a tertiary Canadian ED. Single variables were evaluated including total number of patients in the ED (census), in beds, in the waiting room, in the treatment area waiting to be assessed, and total inpatient admissions. These were compared with Crowding scores (NEDOCS, EDWIN, ICMED, three regional hospital modifications of NEDOCS) as predictors of crowding. Predictive validity was compared to the reference standard of physician perception of crowding, using receiver operator curve analysis. Results: 144 of 169 potential events were recorded over 2 weeks. Crowding was present in 63.9% of the events. ED census (total number of patients in the ED) was strongly correlated with crowding (AUC = 0.82 with 95% CI = 0.76 - 0.89) and its performance was similar to that of NEDOCS (AUC = 0.80 with 95% CI = 0.76 - 0.90) and a more complex local modification of NEDOCS, the S-SAT (AUC = 0.83, 95% CI = 0.74 - 0.89). Conclusion: The single indicator, ED census was as predictive for the presence of crowding as more complex crowding scores. A two-stage approach to crowding intervention is proposed that first identifies crowding with a real-time ED census statistic followed by investigation of precipitating and modifiable factors. Real time signalling may permit more standardized and effective approaches to manage ED flow.
Suicide rates are elevated among people with Bipolar Disorder (BD), yet there is limited research on the nature of suicide in this population. Intentional overdose (OD) is a common method of suicide, and the objective of this study was to characterize the specifics of OD suicides in a BD cohort, and compare with non-BD suicides.
Coroner records for all cases of OD suicide in Toronto, Canada over a 10-year period (1998-2007) were examined. Data collected included demographics, psychiatric diagnosis and all substances present at lethal levels determined by the coroner to have caused death. Data analysis focused on comparisons between the BD cohort and non-BD suicides.
Suicide by overdose was recorded in 34 people with BD (61.8% female) and 343 people without BD (46.9% female). There were numerous differences between the BD and non-BD suicide cohorts. The BD suicide group was younger, more likely to have made a prior suicide attempt, less likely to have a comorbid medical condition, and more likely to overdose on mood stabilizers or antipsychotics. Carbamazepine was the most frequently identified lethal substance among BD suicides (20.6%), with next most common being diphenhydramine (14.7%) and codeine (14.7%). Lithium was found at lethal levels in only one BD suicide (2.9%) and 3 non-BD suicides (0.9%).
Key differences exist between BD and non-BD groups who suicide by OD, including the types of ingested medications. Improving our understanding of suicide in people with BD will ultimately aid in development of effective, targeted prevention strategies.
While the role of consultants in the policy process has long been a concern for scholars of public administration, public management and political science, empirical studies of policy-related consulting are scarce, with little quantitative data. The country-level case studies in this book shed light for the first time on a number of important but as yet under-researched questions. The first is the actual extent of the use of government consulting in a number of countries, and what have been cross-time developments: to what extent has the use of consultants grown over time, and what are the (political, fiscal-economic, society, policy-related) factors that explain greater or lesser growth in a particular country or sector? The second is the question of what role(s) consultants play in the public sector and how large is the share of these consultants in policy work (policy analysis, policy advice, implementation and evaluation).
Demands made by the UK government for external policy support are big business, where the highest spend on consultants has been calculated at £2 billion in 2003–2004 (NAO 2006), and currently major consultancy firms are active in bidding for six months of Brexit work with a price tag of £1.5 million (Martin 2017). At the same time, the focus has been on review and retrenchment, with a fall in spending to £1.8b in 2005–2006 (NAO 2006), whereby ‘the government is determined to make every taxpayer penny count’ and the ‘Cabinet Office is working to help departments reduce reliance on everything from expensive consultants to print cartridges’ (Gov.uk ). Thus, it seems there is recognition of a contribution to public policy that is beyond ‘in-house’ capacity: ‘when used correctly and in the appropriate circumstances … [they] … can provide great benefit to clients – achieving things that clients do not have the capacity or capability to do themselves’ (NAO 2006: 4).
The use of external consultants by the public sector has been an increasingly relevant area of focus for almost three decades, for both government bodies (ANAO 2001; House of Commons Committee of Public Accounts (UK) 2010) and academics (Bakvis 1997; Perl and White 2002; Saint-Martin 2005; Speers 2007; Howlett, Migone and Seck 2014; Howlett and Migone 2014). This is due to both the costs and the role of private sector entities in shaping policy capacity and policy choice. Aside from the most recent contributions, the main focus has been the financial impact of contracting out this function rather than on understanding how external sources have affected the capacity of departments and other government units (Riddell 2007). There are various reasons for this trend.
As the Introduction to this book has argued, governmental use of consultancy services has long been a concern for scholars of public administration, management and political science (Howlett and Migone 2013a, 2013b; Kipping and Engwall 2003; Graeme and Bowman 2006; Guttman and Willner 1976; Rosenblum and McGillis 197).Although the impact of policy consulting is generally expected to be fairly broad, most of these studies have focused on a narrow set of questions related to the effect of contracting out on levels of public service employment and budgets (Dilulio 2016; Guttman and Willner 1976; GAO 2011) rather than on policy outcomes. Much existing research has focused either on placing this expansion in a historical perspective (McKenna 1995, 1996, 2006), or assessing its underlying causes and consequences (David 2012; Berit and Kieser 2002; McGann 2007).