Creation of a holistic admissions review process: A promising change designed to promote diversity and inclusion in nursing education
Bibliographic record
Abstract
Objective: The holistic admissions process employs a review strategy designed to assess an applicant's unique life experiences together with traditional measures of academic achievement. This process considers a broad range of factors that reflect an applicant's potential for professional success in the nursing field. The purpose of this initiative was to introduce a holistic admissions process at a nursing program that has historically utilized an academic metrics-based approach.Methods: The nursing program identified goals and targets for holistic admissions as a means to evaluate the process and to make informed decisions that support the aim of increased student diversity.Results: A team-based approach was used to develop criteria for holistic admissions. Shared values for characteristics of ideal nursing applicants were discussed first, and a literature review was conducted, focusing on predictors of success in nursing school and on the nursing licensure examination. Criteria were developed aimed at capturing the shared values, predictors of success, while also allowing applicants to ‘tell their stories’ during the application process. A student-facing application and applicant faculty review process were created. Benchmarks for monitoring diversity of the student population were identified.Conclusions: Our faculty task force developed and implemented a holistic admissions review process and generated plans designed to monitor the improved diversity of the student population. Faculty reflections highlight the value of pursuing this change and moving forward with holistic admissions in our nursing program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".