Professionalization Of Empathy And Predictors Of Helping Professionals’ Burnout
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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”.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it