Grief, Anger, and Relationality
Bibliographic record
Abstract
BACKGROUND: Therapeutic emotion work is performed by health care providers as they manage their own feelings as well as those of colleagues and patients as part of efforts to improve the physical and psychosocial health outcomes of patients. It has yet to be examined within the context of traumatic brain injury rehabilitation. OBJECTIVE: To evaluate the impact of a research-based theater intervention on emotion work practices of neurorehabilitation staff. RESEARCH DESIGN: Data were collected at baseline and at 3 and 12 months postintervention in the inpatient neurorehabilitation units of two rehabilitation hospitals in central urban Canada. SUBJECTS: Participants (N = 33) were recruited from nursing, psychology, allied health, recreational therapy, and chaplaincy. MEASURES: Naturalistic observations (N = 204.5 hr) of a range of structured and unstructured activities in public and private areas, and semistructured interviews (N = 87) were conducted. RESULTS: Preintervention analysis indicated emotion work practices were characterized by stringent self-management of empathy, suppression of client grief, adeptness with client anger, and discomfort with reactions of family and spouses. Postintervention analysis indicated significant staff changes in a relationality orientation, specifically improvements in outreach to homosexual and heterosexual family care partners, and support for sexual orientation and intimacy expression. No improvements were demonstrated in grief support. CONCLUSION: Emotion work has yet to be the focus of initiatives to improve neurorehabilitative care. Our findings suggest the dramatic arts are well positioned to improve therapeutic emotion work and effect cultures of best practice. Recommendations are made for interprofessional educational initiatives to improve responses to client grief and potential intimate partner violence.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".