The ethics of service work in a neoliberal healthcare context: doing embodied and “dirty” emotional labor
Bibliographic record
Abstract
Purpose The authors explore how service workers negotiate emotional laboring with “dirty” emotions while trying to meet the demands of neoliberal healthcare. In doing so, the authors theorize emotional labor in the context of healthcare as a type of embodied and emotional “dirty” work. Design/methodology/approach The authors apply interpretative phenomenological analysis (IPA) to their data collected from National Health Service (NHS) workers in the United Kingdom (UK). Findings The authors’ data show that healthcare service workers absorb, contain and quarantine emotional “dirt”, thereby protecting their organization at a cost to their own well-being. Workers also perform embodied practices to try to absolve themselves of their “dirty” labor. Originality/value The authors extend research on emotional “dirty” work and theorize that emotional labor can also be conceptualized as “dirty” work. Further, the authors show that emotionally laboring with “dirty” emotions is an embodied phenomenon, which involves workers absorbing and containing patients' emotional “dirt” to protect the institution (at the expense of their well-being).
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".