Emotion regulation and compassion fatigue in mental health professionals in a context of stress: A longitudinal study
Bibliographic record
Abstract
INTRODUCTION: Mental health professionals (MHP) are exposed to several stressors and have emotionally demanding jobs. They must effectively manage their emotions within their everyday practice. Emotion regulation is therefore a key element in understanding how MHPs can protect themselves psychologically. Abundant research shows that healthy and effective emotion regulation can protect against the negative impact of stress on compassion fatigue. However, this perspective does not consider the dynamic interaction that emotion regulation and compassion fatigue can have over time. A much less researched perspective is how compassion fatigue can change emotion regulation styles over time. The present research focused on this dynamic perspective. OBJECTIVE: We took advantage of the COVID-19 pandemic, a stressful period for MHPs, to study the effect of perceived stress on the direction of the changes in emotion regulation styles and compassion fatigue over time. METHODS: Data on stressors, perceived stress, emotion regulation styles (i.e., dysregulation, integration, and suppression), and compassion fatigue were collected from 390 MHPs at two time points over ten months. RESULTS: Findings from a cross-lagged path analysis suggests that perceived stress predicted increases in dysregulation over time. Moreover, there were bidirectional longitudinal associations between dysregulation and compassion fatigue, with each predicting increases in compassion fatigue and dysregulation over time, respectively. CONCLUSIONS: This study contributes to the limited research on the factors that influence how MHPs regulate their emotions and their susceptibility to compassion fatigue. Implications of emotion regulation for MHPs' own mental health and ability to do their work effectively are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".