Compassion in the Clink: When and How Human Services Workers Overcome Barriers to Care
Bibliographic record
Abstract
A key assumption in past literature has been that human services workers become emotionally distant from their charges (such as clients or patients). Such distancing is said to protect workers from the emotionally draining aspects of the job but creates challenges to feeling and behaving compassionately. Because little is known about when and how compassion occurs under these circumstances, we conducted a multiphased qualitative study of 119 correctional officers in the United States using interviews and observations. Officers’ accounts and our observations of their interactions with inmates included cruel, disciplinary, unemotional, and compassionate treatment. Such treatment varied by the situations that officers faced, and compassion was surprisingly common when inmates were misbehaving—challenging current understanding of the occurrence of compassion at work. Examining officers’ accounts more closely, we uncovered a novel way that we theorize human services workers can be compassionate, even under such difficult circumstances. We find that officers describe engaging in practices in which they (a) relate to others by leveling group-based differences between themselves and their charges and (b) engage in self-protection by shielding themselves from the negative emotions triggered by their charges. We posit that the combined use of such practices offsets different emotional tensions in the work, rather than only providing emotional distance, and in doing so, can foster compassionate treatment under some of the most trying situations and organizational barriers to compassion.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".