Nih undergraduate scholarship program for individuals from disadvantaged backgrounds

In order to qualify for this benefit program, you must be a U. In order to qualify, you must have an annual household income before taxes that is below the following amounts:. Always check with the appropriate managing agency to ensure the most accurate guidelines.

To apply, please visit the Application Center. W Welfare Info. Benefit Finder. Program Requirements: In order to qualify for this benefit program, you must be a U. RLCs also provide student trainees with opportunities to participate in preparing proposals, writing manuscripts, and giving presentations and to join in other activities as they gain experience and can make greater contributions to the research.

Students entering BUILD EXITO from community college partners eventually transfer to Portland State University to continue the program, whereas students at four-year-university partners complete the entire program at their home institutions. Respondents reflected the diverse populations targeted by the initiative: 67 percent were female, 60 percent were first-generation undergraduate students, 53 percent came from a disadvantaged background, 71 percent received need-based financial aid, and 38 percent were underrepresented minorities.

These students reported spending, on average, hours in their labs over the course of an academic year. In addition to lab hours, student trainees reported spending, on average, 63 hours meeting directly with their research mentors. The draft included definitions for each level of the taxonomy, and reviewers were asked to make any edits to the definitions that would make them more comprehensible to undergraduates. Faculty research mentors provided high ratings for both face and content validity.

The survey was emailed to 16 student-mentor pairs via a secure method of survey administration see Table 2. Three mentors had more than one mentee, resulting in 16 students and 13 faculty receiving individual survey links. A total of 14 student trainee responses and 15 mentor responses was received, yielding an overall response rate of There were 13 paired sets of mentor-mentee responses For this second administration, the survey was emailed to 65 student-mentor pairs.

Eleven mentors had more than one student, resulting in 65 students and 51 faculty receiving individual survey links. A total of 47 student trainee responses and 51 mentor responses was received although not all mentors responded, 12 mentors responded for multiple mentees , resulting in an overall response rate of The survey was emailed to student-mentor pairs.

Thirty mentors had more than one student, resulting in students and 83 faculty receiving individual survey links. A total of 95 student trainee responses and 87 faculty responses was received, resulting in an overall response rate of At two sites, paper surveys were administered to supplement the online links. No incentives were offered for survey completion; however, students were encouraged to complete all instruments as part of their participation in the program.

The analyses were designed to answer the four research questions presented above. First, for each given role, the percentage of student trainees with any responsibility for that role was determined, with corresponding percentages based on student self-report and mentor report. Second, for respondents indicating any participation in a particular role score higher than 0 , the mean level of responsibility in that role was computed, again reflecting both student self-report and mentor report. The preceding analyses aggregated all responses higher than 0 across the three administrations.

Third, using only the matching student-mentor response pairs from the third administration, the corresponding means for level of responsibility in each role as reported by students and by mentors were computed, as were percent agreement and kappa scores to assess degree of concordance.

Finally, for the two cohorts of students assessed over time, the initial and final means for each role were compared. All analyses were conducted using R statistical software, version 3. To address research questions 1 and 2, contribution percentages for the three administrations were calculated see Table 3.

These results included all responses. Students and mentors most frequently reported students contributing to data curation, with close to 90 percent of responses indicating at least some involvement in that activity see Table 3.

Other roles endorsed at high rates over 80 percent by both students and mentors included investigation, formal analysis, visualization, and conceptualization. The least common roles involved resources, software, supervision, and funding acquisition, although sizable percentages of students approximately 25 to 50 percent did engage in these activities.

Mean scores across all three administrations for student trainees and their mentors reporting at least some level of involvement in a role students assigned a score greater than 0 also were computed. For example, for the On this scale of 1 little responsibility to 3 primary responsibility , the roles receiving the highest average ratings from both students and mentors were investigation and data curation means: 2.

Other roles for which student trainees had considerable responsibility, with both student and mentor mean ratings at 1. For roles with lower percentages of participating students, such as software, supervision, and funding acquisition, mentees and their mentors still reported notable responsibility, as indicated by student trainee and mentor means above 1.

As shown in Table 3 , the mean levels of engagement in the various research roles reported by student trainees and their mentors were generally aligned. Because the values reported in Table 3 were aggregated over all respondents, they did not provide a strong indicator of the agreement between mentees and mentors within mentoring pairs.

This analysis included students from cohorts 2 and 3, and means were calculated using the full scale of no responsibility 0 to primary responsibility 3. Only two roles, validation and writing—original draft, had mean differences greater than 0. In both cases, students rated themselves slightly lower than the ratings of their mentors. Inter-rater reliability was an additional indicator of agreement. Percent agreements were calculated for each role, selecting this measure for ease of interpretability and appropriateness for the sample well trained on the instrument; McHugh Weighted percent agreements ranged from Table 4 shows the initial and final means and standard deviations by role for the 33 student trainees who completed both administrations.

Due to the small sample of mentors and the number of student RLC transfers in cohort 1, analyses for mentor responses in matched pairs for this cohort were not performed. Means presented in Table 4 use the full scale, with response options ranging from 0 to 3. As shown in Table 4 , the overall mean across all roles increased significantly from 1. Some level of increase in responsibility was observed for 12 of the 14 roles.

Paired-sample t -tests were calculated for each role. The two roles that showed slight declines were data curation and validation, but these changes were not statistically significant.

The initial use of the CREDIT URE provides preliminary evidence regarding the utility of this measure for assessing contributions to research made by undergraduate trainees in long-term research placements.

First, the study demonstrated the feasibility of administering the CREDIT URE via online survey, with overall response rates averaging 73 percent for both student trainees and their research mentors, despite some variability across time points.

Second, the instrument yielded meaningful distinctions between the different research roles as related to student participation and extent of student responsibility, as discussed further below. Third, the level of agreement between students and mentors on role engagement was good in the aggregate and very high in direct comparisons of pairs responding to the same case. Given the degree of corroboration provided by more experienced and objective research mentors, the credibility of student-reported data appears strong.

Finally, analysis of data generated with the CREDIT URE suggests that the instrument can be employed for research and evaluation purposes, with implications for developing and improving research training programs. The current study provides valuable insights regarding the nature and extent of undergraduate student research experiences in RLC placements. Findings indicate that students in long-term RLC placements are gaining broad exposure to a variety of research activities. However, findings also suggest variability in the roles of students corresponding to their general levels of training and experience.

For example, both student trainees and their mentors rated data curation and investigation as the most common roles and those with the highest level of student responsibility. Data curation includes data cleaning, maintenance, and management. Investigation includes performing experiments, including data collection.

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