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Data was de-identified during extraction and uploaded in specific portal for analysis at Antwerp University (AU) in Belgium. The hospital antimicrobial stewardship team received analyzed results from AU after two months through email. Secondary AST data records for both in-patients and out-patients whose majority are cases that never responded to first line antibiotic treatment during the period of 2019 to 2023, were analyzed in this study. Determination of treatment failure was based on the clinician’s judgment and was recorded in the patient case notes as persistence or worsening of signs and symptoms during the course of antibiotic treatment.
Methodology: 2022-23 survey of Asian Americans
Prevalence is the proportion of people in a population (sample) who have an attribute or condition at a specific time point (Mann, 2012) regardless of when the attribute or condition first developed (Wang & Cheng, 2020). Additionally, each study participant’s evaluation is completed at one time-point with no follow-ups (Cummings, 2013), providing a ‘snapshot’ of the sample. Cross-sectional designs can be implemented as an interview or survey and may also collect physiological data and biological samples.
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You could also use them in medical research or when building a marketing strategy, for instance. Cross-sectional studies are usually cheaper to conduct than longitudinal studies, so they are ideal if you have a limited budget. A descriptive cross-sectional survey or study assesses how commonly or frequently the primary variable occurs within a select demographic. This study type is commonly used in clinical research, business-related studies, and population studies. Groups can be affected by cohort differences that arise from the particular experiences of a group of people. For example, individuals born during the same period might witness the same important historical events, but their geographic regions, religious affiliations, political beliefs, and other factors might affect how they perceive such events.
Might Prompt Further Study
By understanding the different types of cross-sectional studies, researchers can select the most appropriate design to obtain reliable and relevant data. Below are four common types of cross-sectional studies, each with its unique focus and application. Cross-sectional studies can be categorized into different types based on their objectives and methodologies. These variations allow researchers to adapt the cross-sectional approach to suit specific research questions and contexts.
Cross-Sectional vs. Longitudinal Studies
The cross-sectional design is an appropriate method to determine the prevalence of a disease, attribute, or phenomena in a study sample. The design provides a ‘snapshot” of the sample, and investigators can describe their study sample and review associations between the collected variables (independent and dependent). The observational nature makes it relatively quick to complete a study and provides data to support future studies that might lead to methods to treat or prevent diseases or conditions. The design’s inherent nature makes it inexpensive to conduct and can yield multiple independent (predictor) and dependent (outcome) variables (Cummings, 2013). The data collected can lead to additional studies to build upon the knowledge obtained.
An Illustrated Cross Section of Hong Kong's Infamous Kowloon Walled City — Colossal - Colossal
An Illustrated Cross Section of Hong Kong's Infamous Kowloon Walled City — Colossal.
Posted: Tue, 04 Nov 2014 08:00:00 GMT [source]
Data collection and ethical considerations
One of the advantages of cross-sectional studies is that data is collected all at once, so participants are less likely to quit the study before data is fully collected. Researchers can't always be sure that the conditions a cross-sectional study measures are the result of a particular factor's influence. In many cases, the differences among individuals could be attributed to variation among the study subjects. In this way, cause-and-effect relationships are more difficult to determine in a cross-sectional study than they are in a longitudinal study. For example, a university might post a short online survey about library usage habits among biology majors, and the responses would be recorded in a database automatically for later analysis. This is a simple, inexpensive way to encourage participation and gather data across a wide swath of individuals who fit certain criteria.
Unlike longitudinal studies that observe the same subjects over a period of time to detect changes, cross-sectional studies focus on finding relationships and prevalences within a predefined snapshot. This method is particularly useful for understanding the current status of a phenomenon or to identify associations between variables without inferring causal relationships. This study used a cross-sectional design, which can only capture the associations between variables at one point in time. Therefore, understanding the causality of the relationships between variables requires further studies. The survey was carried out at three Korean universities using a non-probability convenience sampling design, which calls into question the generalizability of the findings.
Are cross-sectional studies quantitative or qualitative?
These studies focus on 'what exists' or 'what is prevalent' without delving into relationships between variables or concepts. A reporting guideline for cross-sectional studies is available for investigators and consumers of research to use. A reporting guideline’s primary goal is to ensure that published clinical research studies provide transparency in reporting a study’s conduct (what was done) and results.
Cross-sectional studies capture data at a one-time point, while longitudinal studies track the same individuals over an extended period to observe changes. If a significant number of men from a particular age group are more prone to have the disease, the researcher can conduct further studies to understand the reasons. A longitudinal study is best used, in this case, to study the same participants over time. For example, suppose you’re interested in how respondent age or gender affects their opinions about vaping and health. In that case, you might gather both demographic and opinion-related data, allowing you to conduct correlational analysis across these variables. Generally aim to provide estimates of the characteristics of a sample, their attitudes, behaviors, or traits.
This is a set back as clinicians will continue to prescribe less effective antibiotics based on availability or affordability. Regarding PPS, our interest was to determine frequency of antibiotics use as well as compliance with standard guidelines. The findings revealed that ceftriaxone (54.3%) and metronidazole (23.3%) were the most commonly used antibiotics in the wardsFootnote 1. Other antibiotics were gentamycin (5.3%), flucloxacillin (4.3%) and penicillin G (4.2%) (Fig. 2). Stockouts of essential antibiotics such as ceftriaxone and penicillin G were reported during PPS data collection.
The next step was adjusting for nonresponse for households without a completed screener interview to create a final household weight. This adjustment allocated the weights of nonrespondents (category 2) to those of respondents (category 1) within classes defined by the cross-classification of sampling strata, census region, and sample type (e.g., ABS and list supplemental samples). Those classes with fewer than 50 sampled addresses or large adjustment factors were collapsed with nearby cells within the sample type. Given the large variance in the household weights among the medium density ABS stratum, final household weights for addresses within this stratum were capped at 300.
The similarity in the ranking of simulation design sub-factor scores in this study and previous studies suggests that efforts are needed to strengthen the simulation design capabilities of Korean nurse educators. To increase fidelity, simulation technology support should be provided at the school level. In addition, since simulation is a recently utilized teaching method in Korean nursing education, most nurse educators lack experience in simulation education.
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