Les grands utilisateurs des services psychiatriques d’urgence dans la région du Montréal métropolitain
Bibliographic record
Abstract
This study lies within the framework of an earlier descriptive study related to psychiatric problems in the Montreal métropolitain area. That study showed that there is a high level of re-utilization of emergency services by people who have had or still have serious mental health problems. This phenomenon is investigated in the present study with a view firstly for describing the incidence of the re-utilization of emergency services in the 6 A social/health region and secondly, for showing the contribution of certains factors that, from the viewpoint of socio-psychiatric features of a target group of patients, are presupposed determinants of re-utilization. The analysis of a collection of socio-demographic and clinical is based on a sample of 1 259 patients re-utilizers of emergency resources. On one hand, such an analysis provided a certain number of factual data for describing the types of re-utilization profiles and identifying the group of patients concerned. On the other hand, the proposed conceptual pattern aims at situating the various factors involved and contributes therefore to defining a perspective that would consider the dynamics various components of the re-utilization (general model). Our hypothesis to the effect that the social-demographic and clinical variables associated to the socio-psychiatric profiles of known patients in the public network only accounts for a weak proportion of the re-utilization, is confirmed. This important conclusion suggests that the other field of determination, particularly the functioning of our system of care distribution (continuity of care, follow up, procedures theories within the framework on non-institutional services) and the difficulties of réintégration encountered by people identified as being < > (community's tolerance) would be the most determinant factors of re-utilization. The present discussion proposes several ways of considering the necessity for developing a general problematic based on the fact that the phenomenon of re-utilization is both the result of a special type of interdependence between the system of care distribution and the identified patient in the network, as well as the result of the interdependence between this person, his/her illness and his/her social setting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".