Development and Evaluation of an Online Toolkit for Managers of 9-1-1 Emergency Communications Centers to Reduce Occupational Stress

This article describes the development and evaluation of an online workplace stress reduction toolkit for use by managers of 9-1-1 emergency communication centers (ECCs). A three-step process for development and testing of digital learning resources was used: (1) establishing need and focus through ECC manager stakeholder engagement, (2) pretesting of the toolkit with the target ECC manager audience, and (3) toolkit utilization and evaluation. The toolkit was developed in close partnership with stakeholders throughout the entire process. Toolkit usage was documented via registration data. The evaluation utilized an online survey that included closed and open-ended questions, which were analyzed using descriptive statistics and qualitative thematic analysis. Over a 20-month period, 274 people registered for the toolkit and, of those, 184 (67%) accessed the content. Respondents to the evaluation survey (N = 156) scored the toolkit highly on satisfaction, self-efficacy, and perceived utility measures. Survey respondents reported intent to apply toolkit content through the following: providing organizational resources to help workers take better care of themselves (41%); creating a lower stress worksite environment (35%) and sharing resources with staff to (1) reduce stress (19%), (2) support conflict resolution (21%), and (3) prevent and/or stop bullying (17%). In delivering actionable content to ECC managers, the toolkit shows promise in addressing and mitigating occupational stress in ECCs. Further research needs to determine the relationship of this strategy for reducing ECC stress.

Probabilistic Temporal Networks with Ordinary Distributions: Theory, Robustness and Expected Utility

Most existing works in Probabilistic Simple Temporal Networks (PSTNs) base their frameworks on well-defined, parametric probability distributions. Under the operational contexts of both strong and dynamic control, this paper addresses robustness measure of PSTNs, i.e. the execution success probability, where the probability distributions of the contingent durations are ordinary, not necessarily parametric, nor symmetric (e.g. histograms, PERT), as long as these can be discretized. In practice, one would obtain ordinary distributions by considering empirical observations (compiled as histograms), or even hand-drawn by field experts. In this new realm of PSTNs, we study and formally define concepts such as degree of weak/strong/dynamic controllability, robustness under a predefined dispatching protocol, and introduce the concept of PSTN expected execution utility. We also discuss the limitation of existing controllability levels, and propose new levels within dynamic controllability, to better characterize dynamic controllable PSTNs based on based practical complexity considerations. We propose a novel fixed-parameter pseudo-polynomial time computation method to obtain both the success probability and expected utility measures. We apply our computation method to various PSTN datasets, including realistic planetary exploration scenarios in the context of the Mars 2020 rover. Moreover, we propose additional original applications of the method.

Private Stochastic Non-convex Optimization with Improved Utility Rates

We study the differentially private (DP) stochastic nonconvex optimization with a focus on its under-studied utility measures in terms of the expected excess empirical and population risks. While the excess risks are extensively studied for convex optimization, they are rarely studied for nonconvex optimization, especially the expected population risk. For the convex case, recent studies show that it is possible for private optimization to achieve the same order of excess population risk as to the nonprivate optimization under certain conditions. It still remains an open question for the nonconvex case whether such ideal excess population risk is achievable. In this paper, we progress towards an affirmative answer to this open problem: DP nonconvex optimization is indeed capable of achieving the same excess population risk as to the nonprivate algorithm in most common parameter regimes, under certain conditions (i.e., well-conditioned nonconvexity). We achieve such improved utility rates compared to existing results by designing and analyzing the stagewise DP-SGD with early momentum algorithm. We obtain both excess empirical risk and excess population risk to achieve differential privacy. Our algorithm also features the first known results of excess and population risks for DP-SGD with momentum. Experiment results on both shallow and deep neural networks when respectively applied to simple and complex real datasets corroborate the theoretical results.

Adapting preference-based utility measures to capture the impact of cancer treatment-related symptoms

It is important that patient-reported outcome (PRO) measures used to assess cancer therapies adequately capture the benefits and risks experienced by patients, particularly when adverse event profiles differ across therapies. This study explores the case for augmenting preference-based utility measures to capture the impact of cancer treatment-related symptoms. Additional cancer treatment-related items could be specific (e.g., rash) or global. While specific items are easier to describe and understand, their use may miss rarer symptoms and those that are currently unknown but will arise from future medical advancements. The appropriate number of additional items, the independence of those items, and their impact on the psychometric properties of the core instrument require consideration. Alternatively, a global item could encompass all potential treatment-related symptoms, of any treatments for any disease. However, such an item may not be well understood by general public respondents in valuation exercises. Further challenges include the decision about whether to generate de novo value sets for the modified instrument or to map to existing tariffs. The fluctuating and transient nature of treatment-related symptoms may be inconsistent with the methods used in conventional valuation exercises. Fluctuating symptoms could be missed by sub-optimal measure administration timing. The addition of items also poses double-counting risks. In summary, the addition of treatment-related symptom items could increase the sensitivity of existing utility measures to capture known and unknown treatment effects in oncology, while retaining the core domains. However, more research is needed to investigate the challenges, particularly regarding valuation.

Effectiveness measure for TAO model of three-way decisions with interval set

The basic idea of the three-way decisions (3WD) is ‘thinking in threes.’ The TAO (trisecting-acting-outcome) model of 3WD includes three components, trisect a whole into three reasonable regions, devise a corresponding strategy on the trisection, and measure the effectiveness of the outcome. By reviewing existing studies, we found that only a few papers touch upon the third component, i.e., measure the effect. This paper’s principal aim is to present an effectiveness measure framework consisting of three parts: a specific TAO model - Change-based TAO model, interval sets, and utility functions with unique characteristics. Specifically, the change-based TAO model provides a method to measure effectiveness based on the difference before and after applying a strategy or an action. First, we use interval sets to represent these changes when a strategy or an action is applied. These changes correspond to three different intervals. Second, we use the utility measurement method to figure out three change intervals. Namely, different utility measures correspond to the different intervals, concave utility metric, direct utility metric, and convex utility metric, respectively. Third, it aggregates the toll utility through the joint of the three utilities mentioned above. The weights among these three are adjusted by a dual expected utility function that conveys the decision-makers’ preferences. We give an example and experiment highlighting the validity and practicability of the utility measure method in the change-based TAO model of three-way decisions.

How to integrate evidence from patient preference studies into health technology assessment: a critical review and recommendations

Health technology assessment (HTA) agencies vary in their use of quantitative patient preference data (PP) and the extent to which they have formalized this use in their guidelines. Based on the authors' knowledge of the literature, we identified six different PP “use cases” that integrate PP into HTA in five different ways: through endpoint selection, clinical benefit rating, predicting uptake, input into economic evaluation, and a means to weight all HTA criteria. Five types of insight are distinguished across the use cases: understanding what matters to patients, predicting patient choices, estimating the utility generated by treatment benefits, estimating the willingness to pay for treatment benefits, and informing distributional considerations. Summarizing the literature on these use cases, we recommend circumstances in which PP can add value to HTA and the further research and guidance that is required to support the integration of PP in HTA. Where HTA places more emphasis on clinical outcomes, novel endpoints are available; or where there are already many treatment options, PP can add value by helping decision makers to understand what matters to patients. Where uptake is uncertain, PP can be used to estimate uptake probability. Where indication-specific utility functions are required or where existing utility measures fail to capture the value of treatments, PP can be used to generate or supplement existing utility estimates. Where patients are paying out of pocket, PP can be used to estimate willingness to pay.