Enhancing Undergraduate Student Self-efficacy and Learning with a Community Service learning (CSL) Nutrition Workshop Assignment
Bibliographic record
Abstract
Community service learning (CSL) activities in undergraduate programs are associated with improvements in self-efficacy (confidence related to performing a specific task) and academic achievement. This study aimed to understand the impact of a CSL assignment on self-efficacy related to teaching community members about evidence-based nutrition and on the overall learning experience. Students were invited to participate in this mixed-methods study (surveys and focus groups), and the results indicate that the CSL activity not only increased students’ self-efficacy related to nutrition science communication, but also gave students a greater feeling of connection to their community and an opportunity to practice skills needed for future careers. Les activités d’apprentissage par l’engagement communautaire dans les programmes de premier cycle sont liées à une amélioration de l’autoefficacité (la confiance en sa propre capacité d’accomplir une tâche particulière) et du rendement universitaire. La présente étude vise à comprendre l’incidence d’un devoir d’apprentissage par l’engagement communautaire sur l’expérience d’apprentissage dans son ensemble et sur l’autoefficacité au sujet de la nutrition s’appuyant sur des données probantes – par rapport aux membres du corps enseignant. Les étudiants étaient invités à participer à une étude employant des méthodes mixtes (des sondages et des groupes de discussion). Les résultats indiquent que les activités d’apprentissage par l’engagement communautaire augmentent l’autoefficacité en matière de communication de la science de la nutrition. Qui plus est, ces activités donnent aux étudiants un sentiment accru d’appartenance à leur communauté ainsi qu’une occasion de mettre en pratique les compétences requises dans leur future carrière.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".