“Enabling Families to Find Their Own Path” – A Narrative Exploration of the Role of Social Workers When a Child is Receiving End-of-Life Care in Hospital
Bibliographic record
Abstract
Social workers are frontline professionals providing psychosocial care to families when children are receiving end-of-life care. To explore the social work role in this context, data was collected from a cohort of 12 social workers employed at a national children's hospital in Ireland, via three focus groups, with four participants in each group. A two-part narrative process elicited social workers' stories of end-of-life care, facilitating the recounting of experiences in the first part of the focus group and allowing the researcher to explore issues raised, in the second part. Following thematic analysis, an overarching theme "enabling families to find their own path" emerged, with four sub-themes: a) engaging with families and navigating across boundaries; b) mediating end-of-life care; c) negotiating competing discourses; d) enabling preferences to minimize regrets. The theme and sub-themes provide insight into empowerment of families at this critical time. The use of a narrative approach facilitated the emergence of knowledge about how this is achieved in a hospital setting. This article maps the essential components of practice identified in the study and charts the key contribution that social workers make in supporting families, with the purpose of informing future practice within this field.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".