Dying Professions: Exploring Emotion Management among Doctors and Funeral Directors
Bibliographic record
Abstract
There are few more emotive experiences in life than death. Drawing on Arlie Hochschild’s concept of emotional labour, this article compares the emotional responsibilities of two groups of death professionals: doctors and funeral directors. It addresses the lack of comparative studies in the otherwise robust literature concerning emotional labour in the workforce. Through qualitative analysis, I identify how funeral directors and doctors believe they should feel in regard to death, how they manage these feelings, and the related consequences of this emotional labour. This analysis suggests that the emotion management of these professionals is primarily influenced by two key factors: prioritizing the emotions of others and stifling one’s own strong emotions. Differences became apparent in terms of how these factors are managed and what the related emotional consequences may be, due to the respective reliance of the funeral directors on surface acting and the doctors on deep acting emotion management strategies. In the future, it would be helpful to complement existing research with participant observation studies in order to better illuminate the meaning that emotional labour has for individuals in practice. Due to their unique position of encountering death as part of a job, death professionals have much to teach each other, as well as the broader population, about accepting and managing emotions related to mortality.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".