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Attitudes towards the collection and linkage of maltreatment data for research: A qualitative study.International Journal of Population... 2022Factors that affect public and professionals' attitudes towards the collection and linkage of health and other data have been explored in the literature. Thus far there...
INTRODUCTION
Factors that affect public and professionals' attitudes towards the collection and linkage of health and other data have been explored in the literature. Thus far there has been no study exploring attitudes towards the collection of child maltreatment data.
OBJECTIVES
Our aim is to explore attitudes regarding the collection and linkage of maltreatment data for research.
METHODS
Participants included younger mothers, older mothers, care-experienced young people, and professionals who were responsible for recording child maltreatment data. Four face-to-face focus groups were conducted, one with younger mothers (n = 6), one with older mothers (n = 10), and two with care-experienced young people (n = 6 and n = 5). An online focus group was conducted with professionals (n = 10), two of whom additionally participated in telephone interviews. Transcribed audio-recorded data were inductively coded, a portion were double-coded by a second researcher, and thematically analysed.
RESULTS
Three major themes were identified. The first concerned issues of consent, specifically the conditions for providing consent and factors influencing this. The second concerned trust in data security and validity, the organisations and individuals providing and using the data, and how the information provided shapes attitudes. The third theme explored the benefits of research and the researchers' role in child protection. Participants wanted the choice of providing consent for data collection, especially when consenting on behalf of another, but there were concerns that maltreated children were unidentifiable in anonymised datasets. Care-experienced young people were concerned about data collection from Social Services records due to their sensitivity. There was a general lack of understanding about how research data is viewed and the accuracy of records.
CONCLUSIONS
Novel findings in the study were strongly related to the sensitive nature of the topic. The findings may be particularly useful when designing research studies and participant materials and a co-productive approach to this should be taken.
Topics: Adolescent; Attitude; Child; Data Collection; Focus Groups; Humans; Qualitative Research; Trust
PubMed: 35146128
DOI: 10.23889/ijpds.v6i1.1693 -
BMC Pregnancy and Childbirth Mar 2021Observation of care at birth is challenging with multiple, rapid and potentially concurrent events occurring for mother, newborn and placenta. Design of electronic data... (Observational Study)
Observational Study
BACKGROUND
Observation of care at birth is challenging with multiple, rapid and potentially concurrent events occurring for mother, newborn and placenta. Design of electronic data (E-data) collection needs to account for these challenges. The Every Newborn Birth Indicators Research Tracking in Hospitals (EN-BIRTH) was an observational study to assess measurement of indicators for priority maternal and newborn interventions and took place in five hospitals in Bangladesh, Nepal and Tanzania (July 2017-July 2018). E-data tools were required to capture individually-linked, timed observation of care, data extraction from hospital register-records or case-notes, and exit-survey data from women.
METHODS
To evaluate this process for EN-BIRTH, we employed a framework organised around five steps for E-data design, data collection and implementation. Using this framework, a mixed methods evaluation synthesised evidence from study documentation, standard operating procedures, stakeholder meetings and design workshops. We undertook focus group discussions with EN-BIRTH researchers to explore experiences from the three different country teams (November-December 2019). Results were organised according to the five a priori steps.
RESULTS
In accordance with the five-step framework, we found: 1) Selection of data collection approach and software: user-centred design principles were applied to meet the challenges for observation of rapid, concurrent events around the time of birth with time-stamping. 2) Design of data collection tools and programming: required extensive pilot testing of tools to be user-focused and to include in-built error messages and data quality alerts. 3) Recruitment and training of data collectors: standardised with an interactive training package including pre/post-course assessment. 4) Data collection, quality assurance, and management: real-time quality assessments with a tracking dashboard and double observation/data extraction for a 5% case subset, were incorporated as part of quality assurance. Internet-based synchronisation during data collection posed intermittent challenges. 5) Data management, cleaning and analysis: E-data collection was perceived to improve data quality and reduce time cleaning.
CONCLUSIONS
The E-Data system, custom-built for EN-BIRTH, was valued by the site teams, particularly for time-stamped clinical observation of complex multiple simultaneous events at birth, without which the study objectives could not have been met. However before selection of a custom-built E-data tool, the development time, higher training and IT support needs, and connectivity challenges need to be considered against the proposed study or programme's purpose, and currently available E-data tool options.
Topics: Bangladesh; Data Accuracy; Electronic Health Records; Female; Focus Groups; Hospital Information Systems; Hospitals; Humans; Infant, Newborn; Nepal; Perinatal Care; Pregnancy; Software; Surveys and Questionnaires; Tanzania
PubMed: 33765951
DOI: 10.1186/s12884-020-03426-5 -
The International Journal of Artificial... Oct 2016Dialysis is a highly quantitative therapy involving large volumes of both clinical and technical data. While automated data collection has been implemented for chronic... (Review)
Review
PURPOSE
Dialysis is a highly quantitative therapy involving large volumes of both clinical and technical data. While automated data collection has been implemented for chronic dialysis, this has not been done for acute kidney injury patients treated with continuous renal replacement therapy (CRRT).
METHODS
After a brief review of the fundamental aspects of electronic medical records (EMRs), a new tool designed to provide clinicians with individualized CRRT treatment data is analyzed, with emphasis on its quality assurance capabilities.
RESULTS
The first platform addressing the problem of data collection and management with current CRRT machines (Sharesource system; Baxter Healthcare) is described. The system provides connectivity for the Prismaflex CRRT machine and enables both EMR connectivity and therapy analytics with 2 basic components: the connect module and the report module.
CONCLUSIONS
The enormous amount of data in CRRT should be collected and analyzed to enable adequate clinical decisions. Current CRRT technology presents significant limitations with consequent lack of rigorous analysis of technical data and relevant feedback. From a quality assurance perspective, these limitations preclude any systematic assessment of prescription and delivery trends that may be adversely affecting clinical outcomes. A detailed assessment of current practice limitations is provided together with several possible ways to address such limitations by a new technical tool.
Topics: Acute Kidney Injury; Data Collection; Humans; Renal Replacement Therapy
PubMed: 27748946
DOI: 10.5301/ijao.5000522 -
Revista Gaucha de Enfermagem 2020To identify and map the online data collection strategies used in qualitative researches in the health field. (Review)
Review
OBJECTIVE
To identify and map the online data collection strategies used in qualitative researches in the health field.
METHODS
This is a scoping review guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) from the Joanna Briggs Institute. We analyzed scientific articles, theses and dissertations from 12 databases. The analysis was made by descriptive statistics.
RESULTS
The final sample consisted of 121 researches. It was found that the number of publications increased sharply in the last five years, with predominance of studies from the United Kingdom. The highlight fields were psychology (28.1%), medicine (25.6%) and nursing (12.4%). The publications used 10 online data collection strategies: Online questionnaires, online forums, Facebook, websites, blogs, e-mail, online focus group, Twitter, chats, and YouTube.
CONCLUSIONS
Online data collection strategies are constantly expanding and increasingly used in the health area.
Topics: Data Collection; Databases, Factual; Humans; Internet; Qualitative Research
PubMed: 32555956
DOI: 10.1590/1983-1447.2020.20190297 -
PLoS Biology Dec 2020Researchers face many, often seemingly arbitrary, choices in formulating hypotheses, designing protocols, collecting data, analyzing data, and reporting results....
Researchers face many, often seemingly arbitrary, choices in formulating hypotheses, designing protocols, collecting data, analyzing data, and reporting results. Opportunistic use of "researcher degrees of freedom" aimed at obtaining statistical significance increases the likelihood of obtaining and publishing false-positive results and overestimated effect sizes. Preregistration is a mechanism for reducing such degrees of freedom by specifying designs and analysis plans before observing the research outcomes. The effectiveness of preregistration may depend, in part, on whether the process facilitates sufficiently specific articulation of such plans. In this preregistered study, we compared 2 formats of preregistration available on the OSF: Standard Pre-Data Collection Registration and Prereg Challenge Registration (now called "OSF Preregistration," http://osf.io/prereg/). The Prereg Challenge format was a "structured" workflow with detailed instructions and an independent review to confirm completeness; the "Standard" format was "unstructured" with minimal direct guidance to give researchers flexibility for what to prespecify. Results of comparing random samples of 53 preregistrations from each format indicate that the "structured" format restricted the opportunistic use of researcher degrees of freedom better (Cliff's Delta = 0.49) than the "unstructured" format, but neither eliminated all researcher degrees of freedom. We also observed very low concordance among coders about the number of hypotheses (14%), indicating that they are often not clearly stated. We conclude that effective preregistration is challenging, and registration formats that provide effective guidance may improve the quality of research.
Topics: Data Collection; Humans; Quality Control; Registries; Research Design
PubMed: 33296358
DOI: 10.1371/journal.pbio.3000937 -
Journal of Medical Internet Research Mar 2023Digital phenotyping refers to near-real-time data collection from personal digital devices, particularly smartphones, to better quantify the human phenotype. Methodology...
Digital phenotyping refers to near-real-time data collection from personal digital devices, particularly smartphones, to better quantify the human phenotype. Methodology using smartphones is often considered the gold standard by many for passive data collection within the field of digital phenotyping, which limits its applications mainly to adults or adolescents who use smartphones. However, other technologies, such as wearable devices, have evolved considerably in recent years to provide similar or better quality passive physiologic data of clinical relevance, thus expanding the potential of digital phenotyping applications to other patient populations. In this perspective, we argue for the continued expansion of digital phenotyping to include other potential gold standards in addition to smartphones and provide examples of currently excluded technologies and populations who may uniquely benefit from this technology.
Topics: Adult; Adolescent; Humans; Smartphone; Wearable Electronic Devices; Data Collection; Phenotype; Data Accuracy
PubMed: 36917148
DOI: 10.2196/39546 -
Journal of the American Medical... Jan 2019Lesbian, gay, bisexual, transgender, and queer (LGBTQ) people experience significant health disparities across the life course and require health care that addresses...
Lesbian, gay, bisexual, transgender, and queer (LGBTQ) people experience significant health disparities across the life course and require health care that addresses their unique needs. Collecting information on the sexual orientation and gender identity (SO/GI) of patients and entering SO/GI data in electronic health records has been recommended by the Institute of Medicine, the Joint Commission, and the Health Resources and Services Administration as fundamental to improving access to and quality of care for LGBTQ people. Most healthcare organizations, however, have yet to implement a system to collect SO/GI data due to multiple barriers. This report addresses those concerns by presenting recommendations for planning and implementing high-quality SO/GI data collection in primary care and other health care practices based on current evidence and best practices developed by a federally qualified health center and leader in LGBTQ health care.
Topics: Data Collection; Electronic Health Records; Female; Gender Identity; Humans; Male; Sexual Behavior; Sexual and Gender Minorities
PubMed: 30445621
DOI: 10.1093/jamia/ocy137 -
BMC Research Notes Aug 2019Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a...
OBJECTIVE
Electronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category.
RESULTS
Data from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1370/12,530). Overall 64% (1499/2352) of all discrepancies were due to data omissions, 76.6% (1148/1499) of missing entries were among categorical data. Omissions in PBDC (n = 1002) were twice as frequent as in EDC (n = 497, p < 0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.
Topics: Biomedical Research; Data Collection; Electronic Health Records; Humans; Nepal; Publications; Reproducibility of Results; Surveys and Questionnaires; Text Messaging
PubMed: 31439025
DOI: 10.1186/s13104-019-4574-8 -
Sensors (Basel, Switzerland) Dec 2022Hourly traffic volumes, collected by automatic traffic recorders (ATRs), are of paramount importance since they are used to calculate average annual daily traffic (AADT)...
Hourly traffic volumes, collected by automatic traffic recorders (ATRs), are of paramount importance since they are used to calculate average annual daily traffic (AADT) and design hourly volume (DHV). Hence, it is necessary to ensure the quality of the collected data. Unfortunately, ATRs malfunction occasionally, resulting in missing data, as well as unreliable counts. This naturally has an impact on the accuracy of the key parameters derived from the hourly counts. This study aims to solve this problem. ATR data from New South Wales, Australia was screened for irregularities and invalid entries. A total of 25% of the reliable data was randomly selected to test thirteen different imputation methods. Two scenarios for data omission, i.e., 25% and 100%, were analyzed. Results indicated that missForest outperformed other imputation methods; hence, it was used to impute the actual missing data to complete the dataset. AADT values were calculated from both original counts before imputation and completed counts after imputation. AADT values from imputed data were slightly higher. The average daily volumes when plotted validated the quality of imputed data, as the annual trends demonstrated a relatively better fit.
Topics: Data Collection; Australia
PubMed: 36560244
DOI: 10.3390/s22249876 -
BMC Medical Informatics and Decision... Apr 2022Electronic sources (eSources) can improve data quality and reduce clinical trial costs. Our team has developed an innovative eSource record (ESR) system in China. This...
BACKGROUND
Electronic sources (eSources) can improve data quality and reduce clinical trial costs. Our team has developed an innovative eSource record (ESR) system in China. This study aims to evaluate the efficiency, quality, and system performance of the ESR system in data collection and data transcription.
METHODS
The study used time efficiency and data transcription accuracy indicators to compare the eSource and non-eSource data collection workflows in a real-world study (RWS). The two processes are traditional data collection and manual transcription (the non-eSource method) and the ESR-based source data collection and electronic transmission (the eSource method). Through the system usability scale (SUS) and other characteristic evaluation scales (system security, system compatibility, record quality), the participants' experience of using ESR was evaluated.
RESULTS
In terms of the source data collection (the total time required for writing electronic medical records (EMRs)), the ESR system can reduce the time required by 39% on average compared to the EMR system. In terms of data transcription (electronic case report form (eCRF) filling and verification), the ESR can reduce the time required by 80% compared to the non-eSource method (difference: 223 ± 21 s). The ESR accuracy in filling the eCRF field is 96.92%. The SUS score of ESR is 66.9 ± 16.7, which is at the D level and thus very close to the acceptable margin, indicating that optimization work is needed.
CONCLUSIONS
This preliminary evaluation shows that in the clinical medical environment, the ESR-based eSource method can improve the efficiency of source data collection and reduce the workload required to complete data transcription.
Topics: Data Accuracy; Data Collection; Electronic Health Records; Humans; Research Design; Workflow
PubMed: 35410214
DOI: 10.1186/s12911-022-01824-7