Prix de conférence à la mémoire de Helene Hudson 2002 : Conversations thérapeutiques face à un cancer persistant ou récidivant
Bibliographic record
Abstract
C’est un grand honneur pour moi que d’avoir été choisie pour présenter la conférence en mémoire de Helene Hudson dans le cadre de la 14 e Conférence annuelle de l’ACIO. Je crois que c’est particulièrement significatif que je le fasse à Winnipeg, la ville natale de Helene et que j’ai la chance de pouvoir le faire devant certaines de ses anciennes collègues et amies. J’ai rencontré quelques-unes d’entre elles et j’ai senti la présence spirituelle de Helene en écoutant leurs récits sur le dévouement avec lequel elle s’appliquait à faire une différence dans la vie des patients atteints de cancer, sur sa compassion, son sens de l’humour et sa joie de vivre d’ensemble. C’est donc en sa mémoire que j’aimerais commencer ma présentation sur les conversations thérapeutiques face à un cancer persistant ou récidivant.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.019 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".