This paper analyzes the nature and perceived effects of mid-stakes testing (known as the EQAO) in Ontario, Canada. Ontario\'s mid-stakes tests were meant to ensure accountability and transparency, and assure system-wide improvement, while avoiding the negative effects and perverse incentives of their high-stakes counterparts. The paper provides new evidence from two projects covering almost a 10-year time-span in 10 of Ontario\'s 72 school districts. It shows that even though mid-stakes testing is milder in its manifestations and effects than high-stakes testing, concerns remain about the need for and side effects of such testing. The findings concern two periods of Ontario educational reform. In the first period, with a specific focus on improving performance in literacy and mathematics, administrators and special education support staff felt that the assessments raised teachers\' expectations and sense of urgency leading to steady improvements in measured achievement, but that there was also evidence of negative effects, especially on paying undue attention to \"bubble\" students just below the threshold for minimum proficiency. In the second reform period focused on broad excellence, well-being and equity as inclusion, mid-stakes tests were perceived as having more widespread negative effects. These included teaching to the test, cultural bias, avoidance of innovation, dilemmas of whether to include highly vulnerable students in the testing process or not, and emotional ill-being among students and teachers. The paper concludes that Ontario\'s twentieth century system of large scale, mid-stakes assessment has not kept pace with its twenty first century commitments to deeper learning and stronger well-being.

BACKGROUND: Although indicator condition (IC)-guided HIV testing (IC-HIVT) is effective at facilitating timely HIV diagnosis, research on IC categories and the related HIV risk in Taiwan is limited. To improve the adoption and spread of IC-HIVT in Taiwan, this study compared the IC categories of people living with HIV (PLWH) and non-HIV controls and investigated delays in the diagnosis of HIV infection.
METHODS: This nationwide, retrospective, 1:10-matched case-control study analyzed data from the Notifiable Diseases Surveillance System and National Health Insurance Research Database to evaluate 42 ICs for the 5-year period preceding a matched HIV diagnostic date from 2009 to 2015. The ICs were divided into category 1 ICs (AIDS-defining opportunistic illnesses [AOIs]), category 2 ICs (diseases associated with impaired immunity or malignancy but not AOIs), category 3 ICs (ICs associated with sexual behaviors), and category 4 ICs (mononucleosis or mononucleosis-like syndrome). Logistic regression was used to evaluate the HIV risk associated with each IC category (at the overall and annual levels) before the index date. Wilcoxon rank-sum test was performed to assess changes in diagnostic delays following an incident IC category by HIV transmission routes.
RESULTS: Fourteen thousand three hundred forty-seven PLWH were matched with 143,470 non-HIV controls. The prevalence results for all ICs and category 1-4 ICs were, respectively, 42.59%, 11.16%, 15.68%, 26.48%, and 0.97% among PLWH and 8.73%, 1.05%, 4.53%, 3.69%, and 0.02% among non-HIV controls (all P < 0.001). Each IC category posed a significantly higher risk of HIV infection overall and annually. The median (interquartile range) potential delay in HIV diagnosis was 15 (7-44), 324.5 (36-947), 234 (13-976), and 74 (33-476) days for category 1-4 ICs, respectively. Except for category 1 for men who have sex with men, these values remained stable across 2009-2015, regardless of the HIV transmission route.
CONCLUSIONS: Given the ongoing HIV diagnostic delay, IC-HIVT should be upgraded and adapted to each IC category to enhance early HIV diagnosis.

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Digital health tools may facilitate the continuity of care. Enhancement of digital aid is imperative to prevent information gaps or redundancies, as well as to facilitate support of flexible care plans.
The study presents Health Circuit, an adaptive case management approach that empowers health care professionals and patients to implement personalized evidence-based interventions, thanks to dynamic communication channels and patient-centered service workflows; analyze the health care impact; and determine its usability and acceptability among health care professionals and patients.
From September 2019 to March 2020, the health impact, usability (measured with the system usability scale; SUS), and acceptability (measured with the net promoter score; NPS) of an initial prototype of Health Circuit were tested in a cluster randomized clinical pilot (n=100) in patients with high risk for hospitalization (study 1). From July 2020 to July 2021, a premarket pilot study of usability (with the SUS) and acceptability (with the NPS) was conducted among 104 high-risk patients undergoing prehabilitation before major surgery (study 2).
In study 1, Health Circuit resulted in a reduction of emergency room visits (4/7, 13% vs 7/16, 44%), enhanced patients\' empowerment (P<.001) and showed good acceptability and usability scores (NPS: 31; SUS: 54/100). In study 2, the NPS was 40 and the SUS was 85/100. The acceptance rate was also high (mean score of 8.4/10).
Health Circuit showed potential for health care value generation and good acceptability and usability despite being a prototype system, prompting the need for testing a completed system in real-world scenarios.
ClinicalTrials.gov NCT04056663; https://clinicaltrials.gov/ct2/show/NCT04056663.

Border control mitigates local infections but bears a heavy economic cost, especially for tourism-reliant countries. While studies have supported the efficacy of border control in suppressing cross-border transmission, the trade-off between costs from imported and secondary cases and from lost economic activities has not been studied. This case study of Singapore during the COVID-19 pandemic aims to understand the impacts of varying quarantine length and testing strategies on the economy and health system. Additionally, we explored the impact of permitting unvaccinated travelers to address emerging equity concerns. We assumed that community transmission is stable and vaccination rates are high enough that inbound travelers are not dissuaded from traveling.
The number of travelers was predicted considering that longer quarantine reduces willingness to travel. A micro-simulation model predicted the number of COVID-19 cases among travelers, the resultant secondary cases, and the probability of being symptomatic in each group. The incremental net monetary benefit (INB) of Singapore was quantified under each border-opening policy compared to pre-opening status, based on tourism receipts, cost/profit from testing and quarantine, and cost and health loss due to COVID-19 cases.
Compared to polymerase chain reaction (PCR), rapid antigen test (ART) detects fewer imported cases but results in fewer secondary cases. Longer quarantine results in fewer cases but lower INB due to reduced tourism receipts. Assuming the proportion of unvaccinated travelers is small (8% locally and 24% globally), allowing unvaccinated travelers will accrue higher INB without exceeding the intensive care unit (ICU) capacity. The highest monthly INB from all travelers is $2,236.24 m, with 46.69 ICU cases per month, achieved with ARTs at pre-departure and on arrival without quarantine. The optimal policy in terms of highest INB is robust under changes to various model assumptions. Among all cost-benefit components, the top driver for INB is tourism receipts.
With high vaccination rates locally and globally alongside stable community transmission, opening borders to travelers regardless of vaccination status will increase economic growth in the destination country. The caseloads remain manageable without exceeding ICU capacity, and costs of cases are offset by the economic value generated from travelers.

BACKGROUND: The COVID-19 pandemic heavily impacted many low- and middle-income countries (LMICs), such as Peru, overwhelming their health systems. Rapid antigen detection self-tests for SARS-CoV-2, the virus that causes COVID-19, have been proposed as a portable, safe, affordable, and easy-to-perform approach to improve early detection and surveillance of SARS-CoV-2 in resource-constrained populations where there are gaps in access to health care.
OBJECTIVE: This study aims to explore decision makers\' values and attitudes around SARS-CoV-2 self-testing.
METHODS: In 2021, we conducted a qualitative study in 2 areas of Peru (urban Lima and rural Valle del Mantaro). Purposive sampling was used to identify representatives of civil society groups (RSCs), health care workers (HCWs), and potential implementers (PIs) to act as informants whose voices would provide a proxy for the public\'s attitudes around self-testing.
RESULTS: In total, 30 informants participated in individual, semistructured interviews (SSIs) and 29 informants participated in 5 focus group discussions (FGDs). Self-tests were considered to represent an approach to increase access to testing that both the rural and urban public in Peru would accept. Results showed that the public would prefer saliva-based self-tests and would prefer to access them in their community pharmacies. In addition, information about how to perform a self-test should be clear for each population subgroup in Peru. The tests should be of high quality and low cost. Health-informed communication strategies must also accompany any introduction of self-testing.
CONCLUSIONS: In Peru, decision makers consider that the public would be willing to accept SARS-CoV-2 self-tests if they are accurate, safe to use, easily available, and affordable. Adequate information about the self-tests\' features and instructions, as well as about postuse access to counseling and care, must be made available through the Ministry of Health in Peru.

OBJECTIVE: To compare the accuracy in the estimation of the Smith machine bench press 1-repetition maximum (1RM) when using a novel minimum velocity threshold (MVT) called optimal MVT (MVT that minimizes the differences between the actual and predicted 1RM in a preliminary session) with respect to using the 2 standard MVTs (general and individual MVTs).
METHODS: A total of 126 young men (Smith machine bench press 1RM = 80.7 [13.6] kg) completed 2 identical sessions consisting of an incremental loading test until reaching the 1RM load. Four individual load-velocity relationships were modeled in each session considering all loading conditions until reaching the load that showed the closest mean velocity to 0.60, 0.50, 0.40, and 0.30 m·s-1. The first testing session was used to determine the preindividual MVT and 4 optimal MVTs (1 for each final test velocity), while the second testing session was used to estimate the 1RM using 4 types of MVT (general MVT, preindividual MVT, actual-individual MVT, and optimal MVT).
RESULTS: The absolute errors in the prediction of the 1RM were significantly lower for the optimal MVT (2.94 [2.40] kg) compared to the general MVT (3.66 [2.99] kg), preindividual MVT (3.80 [3.15] kg), and actual-individual MVT (4.02 [3.21] kg). The optimal MVT (intraclass correlation coefficient [ICC] ranged from .56 to .62) was always more reliable than the individual MVT (ICC = .34).
CONCLUSIONS: The optimal MVT provides more accurate estimates of the Smith machine bench press 1RM than the standard MVTs previously used in scientific research (general and individual MVTs).

BACKGROUND: Coronavirus disease 2019 (COVID-19) became a pandemic within a matter of months. Analysing the first year of the pandemic, data and surveillance gaps have subsequently surfaced. Yet, policy decisions and public trust in their country\'s strategies in combating COVID-19 rely on case numbers, death numbers and other unfamiliar metrics. There are many limitations on COVID-19 case counts internationally, which make cross-country comparisons of raw data and policy responses difficult.
UNASSIGNED: This paper presents and describes steps in the testing and reporting process, with examples from a number of countries of barriers encountered in each step, all of which create an undercount of COVID-19 cases. This work raises factors to consider in COVID-19 data and provides recommendations to inform the current situation with COVID-19 as well as issues to be aware of in future pandemics.

The COVID-19 pandemic highlighted the impact of social inequalities in health (SIH). Various studies have shown significant inequalities in mortality and morbidity associated with COVID-19 and the influence of social determinants of health. The objective of this qualitative case study was to analyze the consideration of SIH in the design of two key COVID-19 prevention and control interventions in France: testing and contact tracing. Interviews were conducted with 36 key informants involved in the design of the intervention and/or the government response to the pandemic as well as relevant documents (n = 15) were reviewed. We applied data triangulation and a hybrid deductive and inductive analysis to analyze the data. Findings revealed the divergent understandings and perspectives about SIH, as well as the challenges associated with consideration for these at the beginning stages of the pandemic. Despite a shared concern for SIH between the participants, an epidemiological frame of reference dominated the design of the intervention. It resulted in a model in which consideration for SIH appeared as a complement, with a clinical goal of the intervention: breaking the chain of COVID-19 transmission. Although the COVID-19 health crisis highlighted the importance of SIH, it did not appear to be an opportunity to further their consideration in response efforts. This article provides original insights into consideration for SIH in the design of testing and contact-tracing interventions based upon a qualitative investigation.
The COVID-19 pandemic has highlighted the importance of social inequalities in health (SIH) and the disproportionate burden of the pandemic and its consequences related to socioeconomic status, ethnicity and race, among other determinants of health. Public health interventions are likely to increase SIH when they are not considered in the design phase. Through a qualitative case study, we analyzed the design of one of the first local initiative providing testing and contact tracing offer to the general population in the Île-de-France region (Paris region, France) in response to the COVID-19 pandemic. This article discusses the uncertainty and challenges associated with consideration for SIH in the intervention design. It explores the diverse understandings of SIH among the actors and the complexities of cross-sectoral partnerships addressing SIH in times of health crisis. Despite a consensual concern for this issue among the respondents, an epidemiological frame of reference dominated the intervention design. It resulted in a model in which consideration for SIH appeared as a complement, with a clinical goal of the intervention: breaking the chain of COVID-19 transmission.

We distinguish two general modes of testing for Deep Neural Networks (DNNs): Offline testing where DNNs are tested as individual units based on test datasets obtained without involving the DNNs under test, and online testing where DNNs are embedded into a specific application environment and tested in a closed-loop mode in interaction with the application environment. Typically, DNNs are subjected to both types of testing during their development life cycle where offline testing is applied immediately after DNN training and online testing follows after offline testing and once a DNN is deployed within a specific application environment. In this paper, we study the relationship between offline and online testing. Our goal is to determine how offline testing and online testing differ or complement one another and if offline testing results can be used to help reduce the cost of online testing? Though these questions are generally relevant to all autonomous systems, we study them in the context of automated driving systems where, as study subjects, we use DNNs automating end-to-end controls of steering functions of self-driving vehicles. Our results show that offline testing is less effective than online testing as many safety violations identified by online testing could not be identified by offline testing, while large prediction errors generated by offline testing always led to severe safety violations detectable by online testing. Further, we cannot exploit offline testing results to reduce the cost of online testing in practice since we are not able to identify specific situations where offline testing could be as accurate as online testing in identifying safety requirement violations.