Summer Research Internship Curriculum to Promote Self-Efficacy, Researcher Identity, and Peer-to-Peer Learning: Retrospective Cohort Study
Bibliographic record
Abstract
Background: Common barriers to students' persistence in research include experiencing feelings of exclusion and a lack of belonging, difficulties developing a robust researcher identity, perceptions of racial and social stigma directed toward them, and perceived gaps in research skills, which are particularly pronounced among trainees from groups traditionally underrepresented in research. To address these known barriers, summer research programs have been shown to increase the participation and retention of undergraduate students in research. However, previous programs have focused predominantly on technical knowledge and skills, without integrating an academic enrichment curriculum that promotes professional development by improving students' academic and research communication skills. Objective: This retrospective pre-then-post study aimed to evaluate changes in self-reported ratings of research abilities among a cohort of undergraduate students who participated in a summer research program. Methods: The Human BioMolecular Atlas Program (HuBMAP) piloted the implementation of a web-based academic enrichment curriculum for the Summer 2023 Research Internship cohort, which was comprised of students from groups underrepresented in biomedical artificial intelligence research. HuBMAP, a 400-member research consortium funded by the Common Fund at the National Institutes of Health, offered a 10-week summer research internship that included an academic enrichment curriculum delivered synchronously via the web to all students across multiple sites. The curriculum is intended to support intern self-efficacy, researcher identity development, and peer-to-peer learning. At the end of the internship, students were invited to participate in a web-based survey in which they were asked to rate their academic and research abilities before the internship and as a result of the internship using a modified Entering Research Learning Assessment instrument. Wilcoxon matched-pairs signed rank test was performed to assess the difference in the mean scores per respondent before and after participating in the internship. Results: A total of 14 of the 22 undergraduate students who participated in the internship responded to the survey. The results of the retrospective pre-then-post survey indicated that there was a significant increase in students' self-rated research abilities, evidenced by a significant improvement in the mean scores of the respondents when comparing reported skills self-assessment before and after the internship (improvement: median 1.09, IQR 0.88-1.65; W=52.5, P<.001). After participating in the HuBMAP web-based academic enrichment curriculum, students' self-reported research abilities, including their confidence, their communication and collaboration skills, their self-efficacy in research, and their abilities to set research career goals, increased. Conclusions: Summer internship programs can incorporate an academic enrichment curriculum with small-group peer learning in addition to a laboratory-based experience to facilitate increased student engagement, self-efficacy, and a sense of belonging in the research community. Future research should investigate the impact of academic enrichment curricula and peer mentoring on the long-term retention of students in biomedical research careers, particularly retention of students underrepresented in biomedical fields.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".