Evidence Use at the Regional Health Agency of Ile‐de‐France: Analysis of Practices, Obstacles, and Needs
Bibliographic record
Abstract
ABSTRACT Professionals and decision‐makers in regional or local administrations often face time and resource constraints that hinder evidence‐based decision‐making. This article examines the practices, obstacles, and needs relating to evidence use within the Regional Health Agency of Ile‐de‐France, a decentralized agency of the French Ministry of Health and Access to Care. We used a mixed‐methods action research approach, including an online questionnaire completed by 60 agents. In addition, we conducted 27 semi‐structured interviews with certain staff members. We also conducted an exploratory group interview with three documentalists. The results of the structural equation models show that agents' skills in accessing evidence, institutional support and being a project leader were correlated with evidence use in decision‐making 89% ( p = 0.009), 35% ( p < 0.001), and 48% ( p = 0.031) respectively. Respondents reported having skills in identifying and accessing experiential (84.1%, p = 0.007), contextual (84.8%, p = 0.002), and scientific (73.6%, p = 0.486) knowledge. In addition, 79.5% ( p = 0.274) of respondents stated they felt competent in assessing the reliability and relevance of evidence to inform their decision‐making. Although the quantitative results show that respondents generally declared moderate to moderately high levels of competence in accessing and assessing the quality and reliability of evidence, the qualitative analysis highlights partial discrepancies. Several agents mentioned lacking the necessary skills to access and evaluate evidence effectively. This discrepancy can be explained by several factors, including a subjective overestimation of respondents' ‘competence’ in the closed‐ended questionnaires, resulting in an overall positive self‐perception, as well as obstacles to identifying and accessing scientific knowledge (language barrier), difficulties in accessing paid scientific journals, lack of time on the part of agents, and work overload. These findings highlight a disconnect between agents' perceived competence and actual capabilities in accessing and evaluating evidence. It is therefore essential to go beyond simple self‐reported measurement by using mixed‐method approaches to understand better the complexity of the factors that influence the use of evidence in decision‐making in public health agencies. Building an evidence ecosystem in decentralized organizations could lead to better‐informed policies, reduced social inequality in health, and improved resource allocation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".