Legitimising and rationalising in talk about satisfaction with formal healthcare among bereaved family members
Bibliographic record
Abstract
While there is a fair amount of knowledge regarding substantive features of end of life care that family members desire and appreciate, we lack full understanding of the process whereby family members formulate care evaluations. In this article we draw on an analysis of interview data from 24 bereaved family members to explicate how they interpret their experiences and formulate evaluations of end of life care services. Most participants wove between expressing and legitimising dissatisfaction, and qualifying or diffusing it. This occurred through processes of comparisons against prior care experiences and expectations, personalising (drawing on personal situations and knowledge), collectivising (drawing on conversations with and observations of others) and attempting to understand causes for their negative care experiences and to attribute responsibility. The findings suggest that dissatisfaction might be diffused even where care is experienced negatively, primarily through the acknowledgement of mitigating circumstances. To a lesser extent, some participants attributed responsibility to the 'system' (policy and decision-makers) and individual staff members. The findings are discussed in relation to the theoretical understanding of satisfaction and evaluation processes and how satisfaction data might inform improvements to care quality.
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 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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 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".