A collective narrative of care and complex mental illness
Bibliographic record
Abstract
Caring is a fundamental concept in healthcare, yet it is fraught with challenges for people living with complex mental illnesses (CMI). Many scholars theorize relationality and interdependence in their definitions of care, however, there has paradoxically been a sustained failure to involve the testimonies and voices of the people who clinicians are connected to and have interdependent relationships with. Using the work of feminist ethics of care scholars Berenice Fisher and Joan Tronto, we conceptualize care work through relationality and sensitivity to (in)justice. While foregrounding relationality and justice, we used a collective narrative methodology and collective documentation to create stories of care. By documenting stories of care, we hope to contribute to the conversations on care and caring practices.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.050 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.008 |
| 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".