L’outil d’évaluation multi-clientèle comme mécanisme de contrôle des soins à domicile : une analyse poststructuraliste
Bibliographic record
Abstract
INTRODUCTION AND BACKGROUND: In Quebec (Canada), the Multi-clientele Assessment Tool (Outil d'évaluation multi-clientèle, OEMC) profoundly transformed the practice of home care professionals (HCP), including nurses. Since 2015, all home care patients with a completed OEMC have been counted to assess the performance of services. If performance targets are not reached, funding renewal is threatened, exerting pressure on HCPs. OBJECTIVE: The objective of this article is to review the OEMC's implementation in order to understand its political nature and its impacts on the practice of HCPs and patients' lives. MATERIAL AND METHOD: Drawing on the works of Michel Foucault and Gilles Deleuze, we propose a poststructuralist analysis of OEMC documents. RESULTS: Shifting from disciplinary societies to societies of control, the OEMC insidiously contributes to the regulation of home care services as well as patients' lives. The will of HCPs to apply their field of expertise is in opposition with the OEMC's purposes. DISCUSSION AND CONCLUSION: To not complete the OEMC when it is deemed unnecessary would require a negotiation by HCPs. However, HCPs' autonomy is compromised by discourses repressing all forms of resistance.
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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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".