Holistic Healers of Illnesses Unseen: Psychiatry’s Embodiment of Whole Person Care
Bibliographic record
Abstract
e'll be following up with you after we've had a chance to make a plan, ok? Alright, have a good day." "Doctor, I've never had a good day."The gravity with which these particular words were spoken lingered in my mind, uttered with a stoic conviction and without a hint of hyperbolic flair.Such a comment offered insight into a life marred by housing insecurity, psychiatric illness, and substance use disorder.On my consult liaison psychiatry rotation, I heard many patients describe suffering to an extent I could hardly fathom.I was struck by the degree to which my largely unremarkable, upper-middle class roots differed from my patients' circumstances.At this early stage in my training, I felt this disparity acutely, and worried that my patients did too.I experienced a creeping fear that despite my best efforts, patients might view such a glaring discrepancy as an impediment to the "W
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.061 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.004 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".