“We are to be like machines…fill the bed before it gets cold”: Exploring the emotional geographies of healthcare providers caring for dying residents in long-term care facilities
Bibliographic record
Abstract
The end-of-life context is imbued with emotions, with death and dying transforming everyday places, like long-term care facilities, into entirely new emotional topographies that can evoke profound effects on those who live and work within these settings. Despite their significant role, healthcare providers' emotions and their interconnections with 'place' have received relatively little attention from researchers, including geographers of care and caregiving. This secondary thematic analysis attempts to address this notable gap by exploring the emotional geographies of healthcare providers caring for dying residents in four long-term care facilities in western Canada. By drawing upon interview and focus group data with administrators (n = 12) and direct care provider (n = 80) participants, findings reveal that experiences of caring for dying residents were often charged with negative emotions (e.g., distress, frustration, grief). These emotions were not only influenced by social and physical aspects of 'place', but the temporal process of caring for a dying resident, which included: (1) Identifying a resident as in need of a palliative approach to care; (2) Actively dying; and (3) Following a resident's death. Findings indicate that providers' emotions shifted in scale at each of these temporal phases, ranging from association with the facility as a whole to the micro-scale of the body. Broader structural forces that influence the physical and social place of long-term care facilities were also found to shape experiences of emotional labor among staff. With an increasing number of deaths occurring within long-term care facilities throughout the Global North, such findings contribute critical experiential knowledge that can inform policy and programs on ways to help combat staff burnout, facilitate worker satisfaction, and foster resilience among long-term care providers, ensuring they receive the necessary supports to continue fulfilling this valuable caring role.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".