Emotional Engagement and Caring Relationships: The Assessment of Emotion Regulation Repertoires of Nurses
Bibliographic record
Abstract
In spite of the importance of emotion regulation for nurses' well-being, little is known about which strategies nurses habitually use, how these strategies combine in order to regulate their emotional distress, and how these are related to their caregiving orientations. The current study aimed to explore the emotion regulation repertoires that characterize health-care providers and to investigate the association between these repertoires and caregiving orientations in a sample of nurses. Firstly, a confirmatory factor analyses was run to test the suitability of the Regulation of Emotion System Survey for the assessment of six emotion regulation strategies among health-care providers. Subsequently, the latent profiles analysis was employed to explore emotion regulation repertoires. Three repertoires emerged: The Average, the Suppression Propensity and the Engagement Propensity profiles. The participants of the last two groups relied on Expressive Suppression and Engagement, respectively, more often than others. Nurses were more likely to be placed within the Engagement Propensity group when compared to the first responders, and higher levels of hyperactivation of the Caregiving System were associated with this repertoire. A greater reliance on Expressive Engagement among nurses was discussed in terms of the fact that nurses usually have a longer and more care-oriented relationships with patients than first responders.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".