Metaphors of loss and transition: An appreciative inquiry
Bibliographic record
Abstract
In this qualitative research study, we have used the arts-based research methodology, Appreciative Inquiry, to conduct a broadly based thematic and narrative analysis of art, loss/transition, and healing in formal/institutional and informal/family healthcare settings. Drawing on 21 loosely structured 1-hour interviews with African, British, Canadian and US caregivers, we have identified 13 overlapping themes of loss and healing. We use these themes to assess the broad scope of art in formal and informal care and palliation; to embed loss as an intrinsic health issue; and to consider art's capacity to offer insight and resolution in professional/family care partnerships as well as population health. We suggest that experiential and narrative data offer as valid an “evidence base” as quantitative data to explore the many critical dimensions of art-for-health theory and practice. Our findings underscore the vital and welcome interaction of art and science in global healthcare practice, education, research and policy.
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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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.068 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.006 |
| 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, 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".