Exploring career satisfaction, burnout, and compassion fatigue as indicators of the quality of career engagement of public school educators
Bibliographic record
Abstract
This study explored the experiences of career satisfaction, burnout, and compassion fatigue in public school educators working with students in Primary/Kindergarten through grade 12 in schools in Nova Scotia and West Virginia. The research participants included 184 teachers, counselors, and administrators employed by the Annapolis Valley Regional School Board in Nova Scotia and Monongalia County Board of Education in West Virginia. Participants completed a questionnaire assessing the constructs of compassion satisfaction, burnout, and compassion fatigue that have been conceptualized in this study as indicators of healthy career engagement, career disengagement, and career overengagement, respectively. Participants also responded to a demographic survey and to questionnaires exploring history and residual effects of direct and indirect traumatic experiences. Measures included the Professional Quality of Life: Compassion Satisfaction and Fatigue Subscales - Revision III (ProQOL-CSF-R III), History of Traumatic Experiences (HTE), and Impact of Events Scale - Revised (IES-R) for direct and indirect trauma. Previous career engagement studies with educators focused on career satisfaction and burnout. Very few addressed educator trauma or compassion fatigue. In the current study, evidence of career satisfaction, burnout, and compassion fatigue was found across all educator subgroups. Burnout and compassion fatigue were significantly related with current traumatization status deriving from a history of direct and indirect trauma. Multiple regression analyses provided limited support to the hypothesis that elementary educators would exhibit higher rates of compassion fatigue than middle and high school educators. The hypothesis that classroom teachers at all grade levels would report higher levels of compassion fatigue than counselors and administrators was not supported. Nor was support obtained for the hypothesis that educators with fewer years of experience would be more vulnerable to compassion fatigue than those with lengthier career paths. The inability of demographic characteristics to differentially predict the 26.09% of the sample who scored in the upper quartile for risk for burnout, and 33.15% who scored in the upper quartile for risk for compassion fatigue, suggests that prevention and intervention programs should target all educators across demographic subgroups.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".