In Whose Interest? Nursing Pre-Licensure Educational Approval
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,072 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,008 |
| Communication savante | 0,010 | 0,007 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,007 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,026 | 0,009 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».