Nurses’ experiences providing bereavement follow‐up: an exploratory study using feminist poststructuralism
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To describe the experiences of nurses who provided bereavement follow-up with families after the death of a child or a pregnancy loss and explore facilitators, barriers and challenges. BACKGROUND: Bereavement follow-up after the death of a child has been identified as an indicator of quality end of life care by families and health care professionals. Research suggests communication with bereaved families can be challenging and intimidating for nurses, particularly those who have had limited experience. In-depth information about the personal, professional and institutional experiences of nurses providing this care is lacking. DESIGN: Eight registered nurses with experience in providing bereavement follow-up to families were interviewed. Purposive sampling provided information rich cases. METHODS: Feminist poststructuralism was the guiding theory and methodology used to uncover underlying discourses. This methodology uses the concepts of discourse analysis, subjectivity and agency to enable a critical understanding of the relationships. RESULTS: The nurses described complex interactions between themselves, the families, hospital practices and policy, and social norms around the discourses of death and professionalism. The importance of relationship, self-care and closure, professional boundaries, invisible nature of the practice and institutional support were prominent themes. Insights into the challenges and rewards of providing bereavement follow-up are discussed in the context of power relations, and recommendations for change are offered. CONCLUSIONS: Nurses in the study were strongly committed to providing ongoing care to families who had experienced the death of a child or a pregnancy loss. Relationships were important to bereavement follow-up care, and the connections with families were often emotional for the nurses. RELEVANCE TO CLINICAL PRACTICE: Nurses and other health professionals would benefit from increased support and education related to bereavement and communication with grieving families. Clarity related to institutional policies to support bereavement care is paramount in helping nurses and others in this work.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".