Across the rows are: total number of individual student surveys; total number of unique courses; number of female teachers with non-English and English speaking background; number of male teachers with non-English (NE) and English (E) speaking background; and the number of female and male international (I) and local (L) students.

Distribution of the BDT classifier response for data and for the expected SM background after the background-only fit. The expectations...

Distribution of the BDT classifier response in data and for the expected SM background after the background-only fit, in the...

Number of sources by bin number.

The global market for screening software for background checks is experiencing robust growth, driven by increasing concerns about workplace safety and regulatory compliance across various industries. The rising adoption of cloud-based solutions, offering scalability and cost-effectiveness, further fuels this expansion. While precise market sizing data is unavailable, a logical estimation based on industry trends and comparable markets suggests a current market valuation in the billions of dollars, with a Compound Annual Growth Rate (CAGR) projected between 10% and 15% for the forecast period (2025-2033). This growth is propelled by several factors: the increasing need for efficient and thorough background checks to mitigate risks associated with hiring unsuitable candidates, the growing awareness of potential legal liabilities related to negligent hiring, and the continuous evolution of technologies that enhance the accuracy and speed of background screening processes. The market is segmented by application (SMEs and large enterprises) and deployment type (cloud-based and on-premises), with cloud-based solutions gaining significant traction due to their accessibility and flexible pricing models. Large enterprises, with their stricter compliance requirements and larger workforce, represent a considerable portion of the market, driving demand for sophisticated, integrated screening solutions. Geographic regions like North America and Europe currently dominate the market, fueled by stringent regulations and higher adoption rates, but significant growth potential exists in emerging markets in Asia-Pacific and Middle East & Africa as awareness of best hiring practices increases. However, challenges such as data privacy concerns, escalating costs associated with comprehensive background checks, and the complexities of navigating diverse global regulations pose restraints to market expansion. The competitive landscape is dynamic, with several established players and emerging startups offering a diverse range of solutions catering to specific needs and budgets. The key players compete based on features, pricing, compliance certifications, and integration capabilities with existing HR systems. Future growth will likely be shaped by innovation in areas such as AI-powered candidate screening, improved data analytics for risk assessment, and the integration of background checks with broader HR technology platforms. Furthermore, the market will see increased demand for solutions addressing evolving legal requirements and data security standards, creating opportunities for vendors to differentiate their offerings and capitalize on the ongoing growth of this essential sector.

description: This data set contains personnel data for DOT new hires and recruits. This data is maintained by the current HR and payroll provider (Department of Interior's IBC) and USAJobs. The data contains PII (Employee Name, SSN, Date of Birth, Home Address, etc.), Civil Rights (Disability, Gender, Race) and other sensitive data (Background Investigations and Security Clearance).; abstract: This data set contains personnel data for DOT new hires and recruits. This data is maintained by the current HR and payroll provider (Department of Interior's IBC) and USAJobs. The data contains PII (Employee Name, SSN, Date of Birth, Home Address, etc.), Civil Rights (Disability, Gender, Race) and other sensitive data (Background Investigations and Security Clearance).

This datasheet will collect background information of the participants for the study Effectiveness of Gatekeeper Training Program (GTP) on awareness, attitude, mental help seeking intention and gatekeeper behavior among Koraga tribe: A study protocol

Significant terms (at 5% level) are highlighted in bold font.

Columns indicate student attribute and rows indicate teacher attribute. Confidence intervals are given in brackets, and significant (at 5% level) terms are highlighted in bold font. Confidence intervals not including the value 1 indicates significance.

Values between 0.7-0.8 are generally considered good, 0.8-0.9 is considered excellent whilst 0.9-1 is considered outstanding. The models are excellent at discriminating high SET scores (5-6) from low SET scores (≤ 4), with AUC’s between 0.96–0.99, and good at discriminating very high (6) from SET scores ≤ 5, with AUC’s 0.79-0.89.

Predictive validity of tested models.

Percentage of cases and controls reporting symptoms or statuses within the 24-month period prior to diagnosis.

It is a panel dataset that includes currency and banking crises dataset for 32 emerging economies along with data for some macroeconomic variables.

Demographic information of sampled patients.

Transportation assimilation: The immigrant sample.

Descriptive characteristics of the districts in our dataset.

Average annual costs per patient (USD).

Due to limitations of the search engine, age groups are overlapping and thus the total number of profiles is higher than the (correct) total of profiles.

Transportation assimilation: Length of stay less than 10 years.

Agreement of VA functional status data with the reference standard of research-collected data for assessing dependence in activities of daily living, among patients with 2–4 weeks elapsed between assessments.a