In Whose Interest? Nursing Pre-Licensure Educational Approval
Bibliographic record
Abstract
Purpose- Professions purport to self-regulate in the public interest. Some of the professions granted self-regulatory status are medicine, Law, Medicine, Accountancy, and Engineering. The focus of this study is the nursing professions. Self-regulation includes setting standards of education and approving pre-licensure educational programs thus controlling who is eligible to gain entry into a profession. Governments grant certain professions the responsibility, right, and privilege of self-regulating based on the premise that these groups are best positioned to oversee and control the profession and its members and that they do so in the public interest. There are dissenting opinions and criticisms leveled against self-regulating professions including accusations of protecting their members and not acting in the interest of the public. Background- With few exceptions, the professions are not schooled in, let alone experts in education or evaluation, yet they control the approval of the education pre-licensure program. The education program is foundational to the profession not only because of the content of the program, but also due to the socialization into the profession including the hidden curriculum, both of which transmit the culture and values of the profession. Allsop (2006) reported that public trust of the General Medical Council was strong in most areas of regulation such as education and standards but not in disciplining its members. Given the centrality of education to the professions and the privilege of approving the pre-licensure education program, what educational supports do professionals need to fulfill this obligation in the public interest? Method and methodology- This qualitative interpretive study surveyed a purposive sample of eight participants regarding their experience with and knowledge of their respective nursing prelicensure education program approval process. Participants included nurse regulator CEOs and Nursing Education Program Approval Committee (NEPAC) members. I explored how committee members are (a) selected, oriented, and educated for this process, (b) the program evaluation or approval education of committee members, (i) prior to joining the committee, and (ii) their perceived competence and (iii) satisfaction with the process at the end of their mission. I used Reflective Thematic Analysis to generate themes from the data. Findings- No participant had formal education in program approval in their undergraduate or graduate level programs. Nurse regulator participants provided an orientation to and materials supporting program approval. Nurses’ pre-licensure education inculcates ethics and values of the profession and an orientation to public service and serving in the public interest. Every participant referred to structural elements such as the Act, Bylaws, the Entry Level Standards of Practice, or Competencies to guide their own work and that of the NEPAC. My conclusion is that profession-led nursing education approval was carried out in the public interest.
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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 | 0.012 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.026 | 0.009 |
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".