Attachment anxiety, reflective functioning and well‐being as predictors of burn‐out and psychological distress among psychotherapists: A longitudinal study
Bibliographic record
Abstract
OBJECTIVE: This study aims to longitudinally investigate the effects of individual's factors on subsequent burn-out/psychological distress in a sample of mental health practitioners, testing if higher attachment anxiety and avoidance and lower reflective functioning (i.e., certainty and uncertainty of mental states) and well-being at baseline may lead to a greater psychological distress and burn-out 1 year later. METHODS: The sample consisted of 40 experienced psychotherapists (females: 72.5%; mean age: 47.40 ± 9.48 years) who completed a battery of questionnaires at baseline and 1 year later. Statistical analyses were conducted with Bayesian multiple linear regressions. RESULTS: Greater attachment anxiety and certainty about mental states and lower individual's well-being at baseline predicted greater burn-out 1 year later. Similarly, greater attachment anxiety and lower individual's well-being at baseline predicted psychological distress at 1 year follow-up. Of note, uncertainty of mental states and avoidant attachment were not associated with outcomes. CONCLUSION: These findings suggest that the levels of burn-out and psychological distress among psychotherapists may be alleviated with interventions targeting attachment insecurity, specific aspects of reflective functioning (i.e., certainty about mental states) and well-being.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".