Professionalization Of Empathy And Predictors Of Helping Professionals’ Burnout
Bibliographic record
Abstract
The article presents the results of a study of empathy in connection with the severity of symptoms of burnout among nurses. To assess empathy we used Interpersonal Reactivity Index (IRI) by M. Davis, to measure burnout level — Maslach Burnout Test (MBT). As a result of the regression analysis of the data, the main hypothesis of the study was confirmed: it is the level of personal distress as a phenomenon of empathy dysregulation that contributes to the development of symptoms of helping professionals’ burnout. “Positive” empathic processes (perspective taking, fantasy and empathic concern) could serve as a means of burnout prevention. Personal distress is seen in its relationship with alexithymia (measured by Toronto Alexithymia Scale TAS-20-R) and psychological mindedness (propensity to psychological thinking, measured by Psychological Mindedness Scale by H. Conte) as the characteris- tics that reflect emotional regulation and coping strategies. The work experience of nurses did not act as a predictor of burnout indicators. This article was prepared with the financial support of the Russian Foundation of Hu- manities (project № 15-26-01007) and Belarusian Republican Foundation for Fundamental Research (project №Г15Р-028), international project “Empathy development in socionomic (“helping”) professions”.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".