Evidence-based decision-making within the context of globalization: A “Why–What–How” for leaders and managers of health care organizations
Bibliographic record
Abstract
In the globalized knowledge economy, the challenge of translating knowledge into policy and practice is universal. At the dawn of the 21st century, the clinicians, leaders, and managers of health care organizations are increasingly required to bridge the research-practice gap. A shift from moving evidence to solving problems is due. However, despite a vast literature on the burgeoning field of knowledge translation research, the “evidence-based” issue remains for many health care professionals a day-to-day debate leading to unresolved questions. On one hand, many clinicians still resist to the implementation of evidence-based clinical practice, asking themselves why their current practice should be changed or expanded. On the other hand, many leaders and managers of health care organizations are searching how to keep pace with the demand of actionable knowledge. For example, they are wondering: (a) if managerial and policy innovations are subjected to the same evidentiary standards as clinical innovations, and (b) how they can adapt the scope of evidence-based medicine to the culture, context, and content of health policy and management. This paper focuses on evidence-based health care management within the context of contemporary globalization. In this paper, our heuristic hypothesis is that decision-making process related changes within clinical/managerial/policy environments must be given a socio-historical backdrop. We argue that the relationship between research on the transfer of knowledge and its uptake by clinical, managerial and policy target audiences has undergone a shift, resulting in increasing pressures in health care for intense researcher-practitioner collaboration and the development of “integrative KT platforms” at the crossroads of different fields (the field of knowledge management and the field of knowledge translation). The objectives of this paper are: (a) to provide an answer to the questions that health professionals ask most frequently about “Why” and “How” to bridge the know-do gap, (b) to illustrate by a Canadian example how the PRO-ACTIVE program helps in closing the evidence-based practice gap.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".