<i>Dietetic Prior Learning Assessment:</i> Student and Faculty Experiences
Bibliographic record
Abstract
Prior learning assessment (PLA) is a process for documenting competence gained through formal and informal opportunities. It has been endorsed by adult educators but is not widely used in dietetic education. Graduate students at a Canadian university, who have prior learning relevant to dietetics, are given the opportunity to develop a portfolio documenting competence in lieu of portions of formal internship experience. An exploratory descriptive methodology is used to describe the experiences of graduate students (n=6) and faculty (n=3) who participated in this innovative program from 2000 to 2005. All students successfully completed portfolios for one-third or more of their internship. After completing the documentation process, students participated in semi-structured interviews consisting of 16 open-ended questions. Faculty completed a five-item survey designed to capture their experiences with the PLA process. Student and faculty data were thematically analyzed and several risks and benefits were documented. Results indicate that, overall, both students and faculty viewed the PLA as a positive experience. Because of the limited placements available for practical internship experiences, PLA can be a useful process for the confirmation of dietetic 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 | 0.003 | 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 | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".