When fieldwork falls apart: Navigating disruption from political turmoil in research

Purpose This paper analyzes employees’ perceptions of data collection processes for human resource analytics (HRA). More specifically, we study the effect that information sharing practices have on employees’ attributions (i.e. benevolent vs malevolent) through the perceived legitimacy of data collection and monitoring processes. Moreover, we investigate whether employees’ emotional reaction (i.e. fear of datafication) depends on their perceived legitimacy and attributions. Design/methodology/approach The research is based on a sample of 259 employees operating for an Italian consulting firm that developed and implemented HRA processes in the last 3 years. The hypothesized model has been tested using structural equation modeling (SEM) on Stata 14. Findings This paper demonstrates the mediating role of perceived legitimacy in the relationship between information sharing practices and employees’ benevolent and malevolent attributions about data collection and monitoring processes for HRA practices. Results also reveal that perceived legitimacy predicts employees’ fear of datafication, with benevolent attributions that partially mediate this relationship. Practical implications This research indicates that employees perceive, try to make sense of and emotionally react to HRA processes. Moreover, we reveal the crucial role of information sharing practices and perceived legitimacy in determining employees’ attributions and emotional reactions to data collection and monitoring processes. Originality/value Combining human resource (HR) attributions, HR system strength, information processing and signaling theories, this work explores employees’ perception, attributive processes and emotional reactions to data collection processes for HRA practices.

Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private providers. To generate base cost evidence for evidence-based policymaking the Costing of Health Services in India (CHSI) study was commissioned in 2018 for the price setting of health benefit packages. This paper reports the findings of a process evaluation of the cost data collection in the private hospitals. The process evaluation of health system costing in private hospitals was an exploratory survey with mixed methods (quantitative and qualitative). We used three approaches-an online survey using a semi-structured questionnaire, in-depth interviews, and a review of monitoring data. The process of data collection was assessed in terms of time taken for different aspects, resources used, level and nature of difficulty encountered, challenges and solutions. The mean time taken for data collection in a private hospital was 9.31 (± 1.0) person months including time for obtaining permissions, actual data collection and entry, and addressing queries for data completeness and quality. The longest time was taken to collect data on human resources (30%), while it took the least time for collecting information on building and space (5%). On a scale of 1 (lowest) to 10 (highest) difficulty levels, the data on human resources was the most difficult to collect. This included data on salaries (8), time allocation (5.5) and leaves (5). Cost data from private hospitals is crucial for mixed health systems. Developing formal mechanisms of cost accounting data and data sharing as pre-requisites for empanelment under a national insurance scheme can significantly ease the process of cost data collection.

IntroductionAyushman Bharat Pradhan Mantri Jan Aarogya Yojana (AB PM-JAY) has enabled the Government of India to become a strategic purchaser of health care services from private providers. To generate base cost evidence for evidence-based policymaking the Costing of Health Services in India (CHSI) study was commissioned in 2018 for the price setting of health benefit packages. This paper reports the findings of a process evaluation of the cost data collection in the private hospitals.MethodsThe process evaluation of health system costing in private hospitals was an exploratory survey with mixed methods (quantitative and qualitative). We used three approaches–an online survey using a semi-structured questionnaire, in-depth interviews, and a review of monitoring data. The process of data collection was assessed in terms of time taken for different aspects, resources used, level and nature of difficulty encountered, challenges and solutions.ResultsThe mean time taken for data collection in a private hospital was 9.31 (± 1.0) person months including time for obtaining permissions, actual data collection and entry, and addressing queries for data completeness and quality. The longest time was taken to collect data on human resources (30%), while it took the least time for collecting information on building and space (5%). On a scale of 1 (lowest) to 10 (highest) difficulty levels, the data on human resources was the most difficult to collect. This included data on salaries (8), time allocation (5.5) and leaves (5).DiscussionCost data from private hospitals is crucial for mixed health systems. Developing formal mechanisms of cost accounting data and data sharing as pre-requisites for empanelment under a national insurance scheme can significantly ease the process of cost data collection.

Information about mental health systems is essential for mental health planning to reduce the burden of neuropsychiatric disorders. Unfortunately, many low- and middle-income countries lack systematic information on their mental health systems. The objectives, scope, structure, and contents of mental health assessment and monitoring instruments commonly used in high-income countries may not be appropriate for use in middle- and low-income countries. The World Health Organization (WHO) has recently developed the WHO Assessment Instrument for Mental Health Systems (WHO-AIMS), a comprehensive assessment tool for mental health systems designed for middle- and low-income countries. WHO-AIMS was developed through an iterative process that included input from in-country and international experts on the clarity, content, validity, and feasibility of the instrument, as well as a pilot trial. The resulting instrument, WHO-AIMS 2.2, consists of six domains: policy and legislative framework, mental health services, mental health in primary care, human resources, public information and links with other sectors, and monitoring and research. These domains address the ten recommendations of the World Health Report 2001 through 28 facets and 155 items. All six domains need to be assessed to form a basic, yet broad, picture of a mental health system, with a focus on health sector activities. WHO-AIMS provides essential information for mental health policy and service delivery. Countries will be able to develop information-based mental health policy and plans with clear baseline information and targets. Moreover, they will be able to monitor progress in implementing reform policies, providing community services, and involving consumers, families, and other stakeholders in mental health promotion, prevention, care and rehabilitation. This article provides an overview of the rationale, development process, and potential uses and benefits of WHO-AIMS.

Ensuring the success of local public health workforce assessments: Using a participatory-based research approach with a rural population

Passenger cars are complex technical products. They consist of units, assemblies, and components that are characterized by a certain combination of interacting parts. Additionally, modern passenger cars feature significant numbers of automatic subsystems and automated components: air conditioning, electric heating systems, lighting systems, compartment doors, exterior doors, etc. The process of collecting data on the technical condition of products is to ensure the regularity, reliability, timeliness, and completeness of information. It is known that products most clearly manifest their quality and dependability in operation. A competent organisation of the collection and processing of information on a product’s dependability allows obtaining reliable information on its health and performance. In the course of operation, the connections between individual units and components of passenger cars may become disrupted, the fasteners of individual parts and sensors may become loose, rubber seals may become naturally worn. All of that causes performance decline, as well as malfunctions and failures. Preventing a sharp increase in the number of failures requires performing a number of preventive actions aimed at identifying and eliminating faults, as well as preventing their root causes. First and foremost, such measures include rolling stock maintenance and overhaul. All such activities are strictly regulated in the operating manuals of both a car and its components. The specified life of passenger cars, as well as their components and units, varies roughly from 20 to 40 years. Some components of passenger cars have been in production with no major modifications since the early 2000s. That suggests that a product’s dependability can be evaluated comprehensively throughout the entire life cycle. But that can only be done by collecting and processing a significant amount of information on malfunctions obtained both during the warranty and post-warranty periods. The information is to come from various sources, i.e., operating companies, service depots, car repair plants that carry out overhauls. This most valuable information is to be accumulated and be digitalisable. This paper addresses a number of matters associated with the collection, validation, and recording of faults and failures of passenger car components. Aim. To examine the state-of-the-art systems that collect and process fault data in engineering companies and to suggest algorithmic and methodological solutions to improve the degree of automation of failure information processing. Methods. The paper uses methods of system analysis and software engineering. Conclusions. An algorithm for recording product failures according to incoming documents is proposed. Software solutions have been developed to automate the process of collecting and processing data on malfunctions of passenger car components. The authors examined a method of tracking the warranty fleet required for defining the total operating time as part of calculating the dependability indicators of passenger car components in operation. A failure code list was proposed that takes into account the specificity of the structural relationships between passenger car components.

Coronavirus disease 2019 (COVID-19) has caused a heavy disease burden globally. The impact of process and timing of data collection on the accuracy of estimation of key epidemiological distributions are unclear. Because infection times are typically unobserved, there are relatively few estimates of generation time distribution. We developed a statistical framework to jointly estimate generation time and incubation period from human-to-human transmission pairs, accounting for sampling biases. We applied the framework on 80 laboratory-confirmed human-to-human transmission pairs in China. We further inferred the infectiousness profile, serial interval distribution, proportions of presymptomatic transmission, and basic reproduction number (R0) for COVID-19. The estimated mean incubation period was 4.8 days (95% confidence interval [CI], 4.1-5.6), and mean generation time was 5.7 days (95% CI, 4.8-6.5). The estimated R0 based on the estimated generation time was 2.2 (95% CI, 1.9-2.4). A simulation study suggested that our approach could provide unbiased estimates, insensitive to the width of exposure windows. Properly accounting for the timing and process of data collection is critical to have correct estimates of generation time and incubation period. R0 can be biased when it is derived based on serial interval as the proxy of generation time.

Thanks to the increasing growth of computational power and data availability, the research in machine learning has advanced with tremendous rapidity. Nowadays, the majority of automatic decision making systems are based on data. However, it is well known that machine learning systems can present problematic results if they are built on partial or incomplete data. In fact, in recent years several studies have found a convergence of issues related to the ethics and transparency of these systems in the process of data collection and how they are recorded. Although the process of rigorous data collection and analysis is fundamental in the model design, this step is still largely overlooked by the machine learning community. For this reason, we propose a method of data annotation based on Bayesian statistical inference that aims to warn about the risk of discriminatory results of a given data set. In particular, our method aims to deepen knowledge and promote awareness about the sampling practices employed to create the training set, highlighting that the probability of success or failure conditioned to a minority membership is given by the structure of the data available. We empirically test our system on three datasets commonly accessed by the machine learning community and we investigate the risk of racial discrimination.

High levels of stress and anxiety are common amongst college students, particularly engineering students. Students report lack of sleep, grades, competition, change in lifestyle, and other significant stressors throughout their undergraduate education (1, 2). Stress and anxiety have been shown to negatively impact student experience (3-6), academic performance (6-8), and retention (9). Previous studies have focused on identifying factors that cause individual students stress while completing undergraduate engineering degree programs (1). However, it not well-understood how a culture of stress is perceived and is propagated in engineering programs or how this culture impacts student levels of identification with engineering. Further, the impact of student stress has not been directly considered in engineering regarding recruitment, retention, and success. Therefore, our guiding research question is: Does the engineering culture create stress for students that hinder their engineering identity development? To answer our research question, we designed a sequential mixed methods study with equal priority of quantitative survey data and qualitative individual interviews. Our study participants are undergraduate engineering students across all levels and majors at a large, public university. Our sample goal is 2000 engineering student respondents. We combined three published surveys to build our quantitative data collection instrument, including the Depression Anxiety Stress Scales (DASS), Identification with engineering subscale, and Engineering Department Inclusion Level subscale. The objective of the quantitative instrument is to illuminate individual perceptions of the existence of an engineering stress culture (ESC) and create an efficient tool to measure the impact ESC on engineering identity development. Specifically, we seek to understand the relationships among the following constructs; 1) identification with engineering, 2) stress and anxiety, and 3) feelings of inclusion within their department. The focus of this paper presents the results of the pilot of the proposed instrument with 20 participants and a detailed data collection and analysis process. In an effort to validate our instrument, we conducted a pilot study to refine our data collection process and the results will guide the data collection for the larger study. In addition to identifying relationships among construct, the survey data will be further analyzed to specify which demographics are mediating or moderating factors of these relationships. For example, does a student’s 1st generation status influence their perception of stress or engineering identity development? Our analysis may identify discipline-specific stressors and characterize culture components that promote student anxiety and stress. Our objective is to validate our survey instrument and use it to inform the protocol for the follow-up interviews to gain a deeper understanding of the responses to the survey instrument. Understanding what students view as stressful and how students identify stress as an element of program culture will support the development of interventions to mitigate student stress.

Real-time data collection and analysis processes have been designed and an initial capability is demonstrated for the July, 1994 JTMDSN test data. The JTMDSN is a one-year, distributed interactive simulation (DIS) demonstration sponsored by the Defense Modeling and Simulation Office (DMSO). The JTMDSN is built on past TACCSF and RESA DIS accomplishments such as their War Breaker support, to make two more major contributions to the DIS community. First Army, Navy, Air Force, and National Systems interfaced via tactical data links using signal Protocol Data Units (PDUs). Second, the data was collected online from PDUs and reduced to records of key events which could be used to display mission performance and network performance measures in real-time. This paper focuses on this data collection and analysis capability. The JTMDSN DIS Gateway, including the data collection process, was designed and developed by Martin Marietta Corporation. Data analysis processes are being developed by BDM Engineering Services Company. >

Complex Regional Pain Syndrome (CRPS) is a persistent pain condition with low prevalence. Multi-centre collaborative research is needed to attain sufficient sample sizes for meaningful studies. This international observational study: (1) tested the feasibility and acceptability of collecting outcome data using an agreed core measurement set (2) tested and refined an electronic data management system to collect and manage the data. Adults with CRPS, meeting the Budapest diagnostic clinical criteria, were recruited to the study from 7 international research centres. After informed consent, a questionnaire comprising the core set outcome measures was completed: on paper at baseline (T1), and at 3 or 6 months (T2) using a paper or e-version. Participants and clinicians provided feedback on the data collection process. Clinicians completed the CRPS severity score at T1 and optionally, at T2. Ethical approval was obtained at each international centre. Ninety-eight adults were recruited (female n=66; mean age 46.6 years, range 19-89), of whom 32% chose to receive the T2 questionnaire in an electronic format. Fifty-five participants completed both T1 and T2. Eighteen participants and nine clinicians provided feedback on their data collection experience. This study confirmed the questionnaire core outcome data are feasible and practicable to collect in clinical practice. The electronic data management system provided a robust means of collecting and managing the data across an international population. The findings have informed the final data collection tools and processes which will comprise the first international, clinical research registry and data bank for CRPS.

We conducted an electronic, self-administered cross-sectional survey of all ODOs in Canada from November 2021 to January 2022. We targeted key knowledge holders familiar with the data collection processes within each Canadian ODO known to Canadian Blood Services. Categorical item responses are presented as numbers and proportions. We achieved a 100% response rate from 10 Canadian ODOs. Most data were collected by organ donation coordinators. Only 2 of 10 ODOs reported using scripts explaining why sociodemographic data are being collected or incorporated training in cultural sensitivity for any given variable. A lack of cultural sensitivity training was endorsed by 50% of respondents as a barrier to the collection of sociodemographic variables by ODOs, whereas 40% of respondents identified a lack of training in sociodemographic variable collection as a significant barrier. Few programs routinely collect sufficient data to examine health inequities with an intersectional lens. Most data collection occurs midway through the ODO interaction, creating a missed opportunity to better understand differences in social identities of patients who register their intention to donate in advance or who decline the donation. National standardization of equity-relevant data collection definitions and processes of the collection is needed.

It is known that LADAR imaging can characterize reflective properties of a scene and provide high resolution threedimensional spatial information useful for target classification; however, scanning and processing high resolution LADAR data is extremely time and computational resource consuming. In remote sensing applications, polarization sensitive imagery can improve target-clutter discrimination of man-made objects in a natural background and anomaly detection algorithms have been shown to accurately identify areas of interest in low resolution imagery. In this paper, we investigate the possibility of enabling passively augmented LADAR for target detection by utilizing polarimetric thermal imagery to cue high resolution LADAR scans of anomalous regions of a scene. A statistical outlier detection algorithm is explored with features extracted from passive polarimetric LWIR imagery collected on an outdoor range under various conditions. The data collection process and products are discussed as well as the performance of anomaly detection algorithms for LADAR cueing. In both data collection and image processing, foliage penetration of partially hidden targets is considered. Data analysis shows polarization information of paired systems improves true positive rate and target detection rate with an acceptable false positive rate while greatly reducing LADAR scan time. As a result, a spatial clustering and anomaly ranking system is introduced to prioritize the most likely anomaly among multiple detections; minimizing time consumed performing LADAR scanning and processing.


 
 
 Reflexivity has been recognised as a crucial strategy in the knowledge generating process and applied in qualitative research to legitimate, validate and question research practices and representation, as well as evaluating the quality of qualitative research. Reflexivity in the social work literature have impacted in research and practice. However, the effect of researcher’s perspectives on the data collection and interpretation process by using reflexivity has not been examined in the mental health research in Malaysia. Thus, this paper aims to explore the role of methodological reflexivity in a qualitative research with Chinese women with mental health problems in a residential care setting in Malaysia. The researcher’s and participants’ interaction and experiences, as well as emotional context during interviews that affect the data interpretation and data collection process are discussed. Greater understanding on their experiences in the care centre has been generated by focusing on these women as an “abled-body” rather than people with disabilities. Recognition of the researcher’s feelings and experiences have enriched the research method and analysis, as well as informing the practice for social workers, health practitioners, and students who work with women with mental health problems.
 
 

Mahesh Maskey reveals the disturbing impact of political disruption on womens health and rights in Nepal. He shows that it is linked to deep-seated economic failures resulting in political turmoil and calls for action to change the conditions underlining the frightening increase of violence against women.