Post-traumatic stress, psychological needs and empathy among professional and family caregivers
Bibliographic record
Abstract
Aims: The aim of this study is to examine the effects of post-traumatic stress disorder (PTSD), psychological needs, and empathy (EMP) on burnout (BRN) levels among professional and family caregivers. Methods: The study sample consists of a total of 379 participants, including 197 professional caregivers and 182 family caregivers. Participants were selected using the snowball sampling method. Data were collected using the PTSD checklist for DSM-5 (PCL 5), the Toronto Empathy Questionnaire, the Basic Psychological Needs Scale, the Maslach Burnout Inventory (MBI), and a Socio-Demographic Information Form. The study was conducted using a relational survey model, and structural equation modeling (SEM) was employed to examine both direct and indirect effects. Results: The SEM findings revealed that PTSD had a significant direct effect on BRN, which was partially mediated by psychological needs and EMP. Notably, the mediating role of empathy was more pronounced in the professional caregiver group, where the direct effect of PTSD on BRN dropped by nearly 49% after accounting for EMP and psychological needs. In contrast, this reduction was only 15% among family caregivers, suggesting that professional caregivers are more vulnerable to BRN due to EMP-driven mechanisms. Additionally, the indirect effects were also statistically significant. These findings suggest that unmet psychological needs and high levels of EMP may contribute to increased BRN among caregivers. Conclusion: The findings revealed that both psychological needs and EMP play a partial mediating role in the relationship between PTSD and BRN. As caregivers’ levels of EMP increase, they may become more vulnerable to BRN. Addressing unmet psychological needs and promoting balanced EMP are considered important for protecting the mental health of both professional and family caregivers.
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.
How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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 teacher head, 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".