OP103 Enhancing Legitimacy And Coherent Value Appraisal Across Interventions In Healthcare And Social Services: Strategy Of The Québec Agency
Bibliographic record
Abstract
Introduction The Institut national d’excellence en santé et en services sociaux (INESSS) makes recommendations regarding introduction, coverage, optimal use or withdrawal of interventions in physical and mental health and social services to support fair and reasonable decisions. The objectives of this work were to develop a statement of principles and ethical foundations for a common appraisal framework across diverse interventions, to enhance legitimacy and coherence of evaluation practices, and develop reflective approaches throughout the institution to operationalize the principles. Methods To develop this statement, INESSS reviewed its practices across different units, surveyed the literature on innovative practices and the evolution of HTA, and undertook an extensive internal and external consultative process. The principles are used to develop reflective activities as part of a continuous improvement strategy. Results The adopted approach to value appraisal considers the contributions of interventions to the Triple Aim of health and social services systems as well as their organizational and sociocultural feasibility and impacts (clinical, populational, economic, organizational, sociocultural dimensions). This approach is articulated around five principles including: (i) evaluating the most relevant objects and adapting evaluation modalities; (ii) mobilizing and integrating diverse types of knowledge; (iii) supporting multidimensional deliberation including diverse perspectives; (iv) developing fair and reasonable recommendations; (v) promoting value creation by supporting the implementation of recommendations and re-evaluation. Although all principles contribute to the legitimacy and credibility of the recommendations, which we aim to implement and consolidate through a set of activities, deliberation is an important part of the process that we are striving to improve. A first set of reflective activities are planned to support its operationalization, including: materials to promote a common understanding of the diverse aspects of the deliberation, reflective workshops on selected past projects, and sharing emerging reflections across INESSS units to further continuous improvement in operationalizing the principles. Conclusions Moving forward, INESSS’s strategic intention is to mobilize its staff and collaborators to facilitate the rigorous, agile and coherent application of these principles.
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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 | 0.129 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".