Innovative Approach to Assess Caring in Nursing Skills
Notice bibliographique
Résumé
Through presentation, discussion, and small group work; this session will discuss why caring should be measured in lab skills, describe one approach to implementing measurements of caring, and instruct learners how to apply this approach. Caring is an underlying construct forming a basis of what a nurse does each day, a way of being in connection with the patient. When teaching students how to perform a skill, cultivating how to be in caring connection with the patient needs to be emphasized. Actions, words, and behaviors all contribute to the caring connection between a nurse and a patient (Watson, 2015). When a nurse’s work includes the caring connection, one affirms that nursing is more than just a job (Watson, 2015). Not only do caring behaviors need to be taught, these behaviors also need to be included in skill evaluation rubrics to emphasize the importance of caring. Skill evaluation rubrics are used to demonstrate student competence in a skill before performing that skill with a patient. Traditionally, the framework of a skill rubric focuses on the proficiency of the task being evaluated. However, competency in nursing is more than task proficiency. Students are expected to demonstrate a caring demeanor when the skill is performed in a clinical setting. To do so skill competency rubrics need to be framed with a focus on caring. To switch the focus from only skill proficiency to cultivating the caring connection; a change in the evaluation rubric framework is needed. Rather than inserting components of caring into a traditional skill grading rubric, rubrics developed using the Attributes of Caring developed by Roach (2002) as the evaluation criteria should be utilized. The Attributes of Caring identify specific behaviors demonstrated by the nurse when caring for a patient. Roach’s work included six attributes of caring: compassion, competence, confidence, conscience, commitment, and comportment. Additional Attributes of Caring may be used to meet the needs of a program. After establishing the essential steps of the skill which will be measured, each step of the skill being evaluated is assessed and the associated Attribute of Caring is determined. The evaluation rubric is set up with each row headed by an Attribute as the measurement criteria and the associated steps of the skill are used to describe the measures to evaluate successful achievement. By using Attributes of Caring as evaluation criteria, the importance of caring behaviors is demonstrated. Students are taught and evaluated on actions, words, and behaviors which contribute to the caring connection and faculty are given a language for evaluation based on caring. Competency in nursing is emphasized and evaluated by implementing this approach to lab skill evaluation. References Roach, M. S. (2002). Caring, the human mode of being: A blueprint for the health professions (2nd rev. ed.). Ottawa: Canadian Healthcare Association Press. Watson, J. (2015). Jean Watson’s theory of human caring. In M. C. Smith & M. E. Parker (Eds.), Nursing theories & nursing practice (pp. 321-339). Philadelphia, PA: F.A. Davis Company.
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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 | 0,008 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,029 | 0,007 |
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 ».