Charlotte Tassé et Bernadette Lépine, fondatrices à part entière de l’Institut Albert-Prévost
Bibliographic record
Abstract
In September 1919, just a few weeks after its opening, Dr. Albert Prévost's sanatorium welcomed its first nurse. Charlotte Tassé was only 26 years old. She was coming back from six months of specialized training in the US and had accepted to help for only two weeks. She will stay 44 years! Quickly become essential to Dr. Prévost, who was very busy with his responsibilities at the Université de Montréal and l'Hôpital Notre-Dame, the young nurse established herself as the heart of this small mental health facility. When the neurologist died, in 1926, she ensured that the sanatorium survive, helped by a new young recruit named Bernadette Lépine. Twenty years after, in 1945, the two nurses saved the institution from bankruptcy by buying it with their own funds. Then, they deeply transformed its organisation and reinforced its training offer, managing to transform it, in a few years only, in one of the most important and avant-garde mental health care and training centers in Québec. However, the arrival of a young and ambitious psychiatrist named Camille Laurin, at the end of the 1950s, knocked the long-standing stability of the institution and then quickly made the important role of the nurses forget. Based on the study of unpublished archives, this paper relates the story of these women and their major contribution to the history of Albert-Prévost Institut.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".