Social justice as an expression of caring through holistic admissions in a nursing program: A proposed conceptual model
Bibliographic record
Abstract
AIM: This paper presents HAR as an expression of caring to create social justice within nursing and achieve a workforce that is representative of those being served. BACKGROUND: The lack of diversity within the health professions has been expressly linked in the literature to health disparities among underrepresented and marginalized groups. RECOMMENDATIONS: Recognizing the value of diversity within healthcare has been the impetus for some health profession programs to use holistic admissions review (HAR) in the assessment and evaluation of applicant suitability. While current HAR recommendations in nursing broaden the lens on which criteria should be used to determine applicant suitability beyond standard academic metrics, existing models do not examine applicants' caring capacity. CONCLUSION: Given caring is the essence of nursing, the authors offer a guiding framework to supplement the American Association of Colleges of Nursing criteria for HAR and a model by which nursing applicants are evaluated on their capacity to care.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".