Innovative Approach to Assess Caring in Nursing Skills
Bibliographic record
Abstract
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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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.007 |
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".