Linking Immigrants with Nutrition Knowledge (Project LINK): An Innovative Approach to Improve Cultural Competence in Dietetic Education
Bibliographic record
Abstract
Linking Immigrants with Nutrition Knowledge (Project LINK) was a service-learning cultural competence training programme completed by undergraduate dietetic students enrolled in the University of Saskatchewan’s (USASK) nutrition and dietetic programme. This paper evaluates the impact of participation in the programme on students’ cultural competence. We conducted a cross-sectional survey and qualitative analysis of reflective essays of 107 participants of Project LINK from 2011 to 2014. Cumulative logistic regression models assessed the impact of the intervention on students’ cultural competencies. The Akaike information criterion compared models and Spearman correlation coefficient identified possible correlation among pre- and post-intervention data points. Student reflective essays were analyzed by inductive thematic analysis. All cultural competencies improved comparing pre- and post-participation in Project LINK. Odds of increasing one level of student knowledge were 110 times of that prior to Project LINK. Comparing student competencies before and after Project LINK, the odds of increasing one level of students’ skills were six times greater, five times greater for increasing one level of students’ ability to interact or encounter, and 2.8 times greater for increasing one level of students’ attitude. The results of this study indicate Project LINK has successfully increased cultural competence and underscores the importance of combining opportunities for practical experience in addition to classroom-based training on cultural competence.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 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".