Bibliographic record
Abstract
The Japanese practice an ancient art called Kintsugi. A craftsperson repairs broken pottery with gold or silver rendering it more beautiful than in its original state. Can clinicians engage in “Kintsugi Mind” and thereby emerge from this pandemic integrated and whole? Yuan et al. (2021) conducted a meta-analysis including 88 studies of post-traumatic stress disorder (PTSD) following earlier pandemics and COVID-19. Health care professionals had the highest prevalence (26.9%) compared to infected cases and the public. Another type of trauma is called secondary or vicarious; it occurs when a person bears witness to suffering and death but remains powerless to change it; countless clinicians have experienced this over the past year. It manifests as emotional depletion, anxiety, insomnia, and impaired interpersonal relationships. How can clinicians heal from their exposure to the pandemic? Post-traumatic growth (PTG) is defined as positive psychological changes following trauma. PTG manifests in five areas: appreciation of life, relating to others, personal strength, recognizing new possibilities, and spiritual change. A transformation in the person’s world view and their place in it ensues. For health care professionals who are experiencing emotional distress, insomnia, or manifest PTSD symptoms they may heal by engaging in the six “Rs.” These are: relating, resourcing, repatterning, reprocessing, reflecting, and rituals. Both PTG and these six practices may contribute to Kintsugi Mind. While this appears to place the onus on individuals, it is crucial that leaders in the health care system implement programs enabling HCPs to be restored, rather than broken by this crisis.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".