Copyright: Section on Person-centered Clinical Care Person-centered medicine from deep inside: personal
Bibliographic record
Abstract
I myself have suffered from serious mental illness. This is my story of suffering and recovery. In the year of 2006, I volunteered as a Face of Mental Illness Awareness Week, a Canada wide anti-stigma public service campaign organized—in part—by the Canadian Psychiatric Association [1]. I voluntarily chose to do this public service to fight against the stigma of mental illness. This stigma against illnesses of the mind/brain are the result of both internal stigma (self-stigma), and external stigma (social stigma [2]) and both types of stigma are especially strong in the field of medicine [3]. It is very difficult to know how to describe how depression really feels. So I’ll try to get some much needed assistance from the author Franz Kafka [4]; the painter Salvador Dali; and by using the lyrics of the Beatle John Lennon [5]. A student taking a science degree in college I suffered from what is sometimes called double depression. For a while I functioned fairly well externally: meaning only that I could put one foot in front of other and wasn’t actually flunking out. I did not realize at the time that I was suffering a serious combination of two depressive disorders. I was unlucky enough to be afflicted with a newly diagnosed case of major depressive disorder, yet also—in addition to this very unwelcomed bio-psych-brain condition—I was also suffering a preexisting—more chronic depressive condition called dysthymia. It was not quite a second dose of destructive depressive despair, but it sure felt that way.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.587 | 0.370 |
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".