Working Through Loss: A Critical Discourse Analysis of Physicians’ Grief
Bibliographic record
Abstract
Although grief is an unavoidable aspect of the human experience, it remains largely unspoken in the workplace, including healthcare. Physicians, in particular, minimize their grief due to professional norms and broader societal discourses, often viewing it as trivial or incomparable to the suffering they witness at work. This study examines how physicians narrate their grief to see which discourses are represented, enacted, and sometimes resisted in clinical practice. Physicians ( n = 12) and residents ( n = 5) from Atlantic Canada participated. Two rounds of interviews, 6 months apart, were conducted. Critical discourse analysis was utilized to make sense of the language participants use to describe their grief experiences. Two dominant discursive framings were identified: grief as an interference and grief as an invitation for meaning making. For many, navigating professional responsibilities while experiencing grief generated tensions and contradictions, evoking feelings of disequilibrium and frustration. For some, however, grief prompted deep self-reflection, leading to a shift in mindset or a realignment of values. Residents grappled most with the discursive boundaries of grief expression, unsure about when and with whom it might be acceptable. More experienced physicians articulated greater ease in discussing grief, often framing it as a source of deepened wisdom. Participants’ discursive framing of grief appeared to change alongside their maturation as physicians, suggesting that status and hierarchy influence the extent to which physicians feel empowered to engage in grief-positive discourse. Formal education and institutional support to foster “grief literate” clinical environments could play a valuable role in supporting physician well-being.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".