Knowledge mobilization in the context of health technology assessment: an exploratory case study
Bibliographic record
Abstract
BACKGROUND: Finding measures to enhance the dissemination and implementation of their recommendations has become part of most health technology assessment (HTA) bodies' preoccupations. The Quebec government HTA organization in Canada observed that some of its projects relied on innovative practices in knowledge production and dissemination. A research was commissioned in order to identify what characterized these practices and to establish whether they could be systematized. METHODS: An exploratory case study was conducted during summer and fall 2010 in the HTA agency in order to determine what made the specificity of its context, and to conceptualize an approach to knowledge production and dissemination that was adapted to the mandate and nature of this form of HTA organization. Six projects were selected. For each, the HTA report and complementary documents were analyzed, and semi-structured interviews were carried out. A narrative literature review of the most recent literature reviews of the principal knowledge into practice frameworks (2005-2010) and of articles describing such frameworks (2000-2010) was undertaken. RESULTS AND DISCUSSION: Our observations highlighted an inherent difficulty as regards applying the dominant knowledge translation models to HTA and clinical guidance practices. For the latter, the whole process starts with an evaluation question asked in a problematic situation for which an actionable answer is expected. The objective is to produce the evidence necessary to respond to the decision-maker's request. The practices we have analyzed revealed an approach to knowledge production and dissemination, which was multidimensional, organic, multidirectional, dynamic, and dependent on interactions with stakeholders. Thus, HTA could be considered as a knowledge mobilization process per se. CONCLUSIONS: HTA's purpose is to solve a problem by mobilizing the types of evidence required and the concerned actors, in order to support political, organizational or clinical decision-making. HTA relies on the mediation between contextual, colloquial and scientific evidence, as well as on interactions with stakeholders for recommendation making. Defining HTA as a knowledge mobilization process might contribute to consider the different orders of knowledge, the social, political and ethical dimensions, and the interactions with stakeholders, among the essential components required to respond to the preoccupations, needs and contexts of all actors concerned with the evaluation question's issues.
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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 | 0.225 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".