Narrative care: Unpacking pandemic paradoxes
Bibliographic record
Abstract
During the coronavirus disease 2019 pandemic, public health has issued three interrelated dominant narratives through social media and news outlets: First, to care for others, we must keep physically distant; second, we live in the same world and experience the same pandemic; and third, we will return to normal at some point. These narratives create complexities as they collide with the authors' everyday lives as nurses, educators, and women. This collision creates three paradoxes for us: (a) learning to care by creating physical distance, (b) a sense of togetherness erases inequities, and (c) returning to normal is possible. To inquire into these three paradoxes, we draw on our experiences with Ingrid, an older adult who requires in-home physical care, and Matthew, a man with multiple disabilities including severe oral dyspraxia and developmental delays. We outline how narrative care is a counterstory to the dominant narratives and enables us to find ways to live our lives within the paradoxes. Narrative care allows us, through attention to embodiment, liminality, and imagination, to create forward looking stories. Understanding narrative care within these paradoxes allows us to offer more complex understandings of the ways narrative care can be embodied in our, and others', lives.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.020 | 0.051 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".