Admission Criteria as Predictors of Student Success in a Dental Hygiene Program
Bibliographic record
Abstract
The aims of this study were to assess which prerequisites the dental hygiene faculty at the University of Alberta perceived as essential to success in the dental hygiene program and to determine if students' prerequisite grades and interview scores predicted their success in the program. Academic records of students admitted between 2004 and 2013 were examined in 2016 for prerequisite course grades, interview scores, and junior, senior, and cumulative grade point average (GPA). In addition, course instructors were surveyed about which prerequisites they deemed necessary for their particular subjects. The results showed that every prerequisite course was perceived as necessary at some point in the program. However, most prerequisite course grades were weak predictors of academic performance, with a moderate correlation between cumulative prerequisite GPA and the junior GPA and final cumulative GPA. The interview was also considered necessary for some preclinical and clinical courses. There was no correlation between interview scores and students' academic performance. These findings suggest that, although the interview and prerequisite GPA requirements filtered out unsuitable candidates, they did not predict which students would be successful in the program. More refined methods need to be devised to identify which students are most likely to succeed.
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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".