La fusion des établissements de santé et de services sociaux
Bibliographic record
Abstract
Cet article est le fruit d’une recension des écrits scientifiques et empiriques sur les répercussions des fusions d’établissements de santé et de services sociaux. La consultation des écrits permet de souligner l’absence de consensus entre les différents auteurs consultés quant à la nature des impacts des fusions d’établissements en ce qui a trait à leurs aspects cliniques, professionnels, administratifs, de gestion interne et de gouvernance. Cet article met en lumière l’importance de la problématique de la reconfiguration des services de santé et des services sociaux québécois, ainsi que la nécessité d’en approfondir les enjeux. \n \nThis article is the fruit of an inventory of the scientific and empirical writings on the consequences of fusions of health and social services establishments. The consultation of the writings makes it possible to underline the absence of a consensus between the various authors consulted as for the nature of the impacts of the fusion of establishments concerning clinical, professional, administrative, internal management and ruling aspects. This article clarifies the importance of the problems of reconfiguring the Quebec health and social services as well as the need to look further into what is at stakes for them.
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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".