Associations between self-care practices and psychological adjustment of mental health professionals: a two-wave cross-lagged analysis
Bibliographic record
Abstract
Cultivation of self-care is believed to foster more well-being and to mitigate the psychological difficulties that mental health professionals experience. However, how the well-being and psychological distress of these professionals impact their personal self-care practice is rarely discussed. In fact, studies have yet to investigate whether the use of self-care improves mental health, or whether being in a better place psychologically makes professionals more prone to using self-care (or both). The present study aims to clarify the longitudinal associations between self-care practices and five indicators of psychological adjustment (well-being, posttraumatic growth, anxiety, depression, and compassion fatigue). A sample of 358 mental health professionals were assessed twice (within a 10-month interval). A cross-lagged model tested all associations between self-care and psychological adjustment indicators. Results showed that self-care at T1 predicted increases in well-being and in post-traumatic growth, and a reduction in anxiety and depression at T2. However, only anxiety at T1 significantly predicted greater self-care at T2. No significant cross-lagged associations were found between self-care and compassion fatigue. Overall, findings suggest that implementing self-care is a good way for mental health workers to "take care of themselves." However, more research is needed to understand what leads these workers to use self-care.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".