The enneagram model for nursing competencies development-An exploratory qualitative study
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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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 | 0.014 | 0.010 |
| 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 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".