The enneagram model for nursing competencies development-An exploratory qualitative study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background and objective: Nursing competencies can be enhanced by the development of emotional intelligence, which promotes self-knowledge. Personality models, such as the Enneagram model, have been used to develop self-knowledge, and thus may contribute to increasing emotional intelligence. However, few studies have examined perceptions of the use of the Enneagram model on nursing competencies. This qualitative study aims to explore the perceptions of nursing educators and advanced practice nurses about the impact of Enneagram model training on the development of their professional competencies.Methods: This qualitative study used individual interviews and thematic analysis according to Miles and Huberman’s method. The nine participants were nursing educators and advanced practice nurses. Interviews were conducted between six and eight weeks after the Enneagram model training.Results: Results revealed that the Enneagram model may contribute to developing emotional intelligence. Participants perceived the Enneagram model training as promoting better self-awareness and understanding of others. It could also support the development of nursing competencies: humanistic action, collaboration, clinical leadership and support for learning in practice settings.Conclusions: The use of the Enneagram model could help nurses develop their emotional intelligence and optimize their practice while preserving their mental health. Implications for Nursing Administration: These findings are important for managers responsible for supporting nurses’ competencies and mental health through complex care situations in a context of change.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it