Utilisation et enjeux des données clinico-administratives dans le domaine de la santé mentale et de la dépendance
Bibliographic record
Abstract
Clinical-administrative databanks are a key tool in support of public health decision-making. A number of databanks are available relevant to population needs, resources available, as well as performance indicators. Since the 2000s, considerable efforts have been dedicated to the consolidation of findings and development of tools aimed at improving surveillance with respect to the health status of populations and performance of the social and healthcare system. At the annual congress of the Association francophone pour le savoir (ACFAS), held in 2017 at McGill University, a seminar was organized on the utilization of databanks in mental health and in addiction. This seminar featured an expert discussion on subjects related to: identification of the principal clinical-administrative databanks, the extent of their use, their limitations, and solutions aimed at optimizing the development of databanks to better support the management of services. This article summarizes the content of this seminar. While databanks entail important strengths, including great potential for the generalization of information, they also present limitations regarding their capacity to respond to needs, quality and validation issues, as well as accessibility. Various recommendations were proposed to improve the management of databanks and optimize their impact, including their centralization in a single, and highly accessible autonomous organism, and societal and cultural change favoring performance evaluation in the interest of improving practices and better monitoring health results.
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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 | 0.159 | 0.442 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.024 | 0.032 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".