Compassion Fatigue: Caveat Caregiver?
Bibliographic record
Abstract
(Excerpt) Most of us are familiar with the stereotype of the burned out lawyer who drags herself to work in the morning, makes cynical comments throughout the day, no longer provides her best service to her clients, and goes home bored and uninspired. You may wonder why someone so uncaring ever became a child advocate in the first place, or how she lost her spark. And you know this could never happen to you. Right? Wrong, according to a panel of experts convened by the ABA Section of Litigation’s Children’s Rights Litigation Committee in a teleconference examining the phenomenon recently termed “compassion fatigue.” The teleconference, “Addressing Compassion Fatigue: An Ethical Mandate,” was moderated by Trenny Stovall, Esq., director of the DeKalb County Child Advocacy Center in Decatur, Georgia, and featured: • Alexandra Dolan, MSS, LSW, from the Support Center for Child Advocates in Philadelphia; • Josh Spitalnick, PhD, ABPP, an adjunct assistant professor in psychiatry and behavioral sciences at Emory University in Atlanta; • Françoise Mathieu, M.Ed., CCC., the coexecutive director of TEND in Ontario; and • Danielle Lynch, Esq., the supervising attorney in the DeKalb County Child Advocacy Center in Decatur. This article provides a brief overview of the teleconference as well as the materials.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.064 | 0.012 |
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".