Using knowledge translation as a framework for the design of a research protocol
Bibliographic record
Abstract
Knowledge translation has been defined as the synthesis, dissemination, exchange and ethically sound application of knowledge to improve health, resulting in a stronger health-care system. Using KT activities to aid in the adoption of evidence into practice can address current health-care challenges such as increasing organizational practice standards, alleviating the risk for adverse events and meeting practitioner needs for evidence at the bedside. Two general forms of KT have been identified. These being integrated KT and end-of-grant KT. Integrated KT involves the knowledge users in the research team and in the majority of stages of the research process. End-of-grant KT relates to the translation of findings through a well-developed dissemination plan. This paper describes the process of using an integrated knowledge translation approach to design a research protocol that will examine the effectiveness of a web-based patient educational intervention. It begins with a description of integrated knowledge translation, followed by the presentation of a specific case example in which integrated knowledge translation is used to develop a nursing intervention. The major elements of integrated knowledge translation pertain to need for a knowledge user who represents the broad target user group, and who is knowledgeable in the area under investigation and who as authority to enact changes to practice. Use of knowledge users as equal partners within the research team; exploring all feasible opportunities for knowledge exchange; and working with knowledge users to identify all outcomes related to knowledge translation are the other major elements of integrated knowledge translation that are addressed throughout this paper. Furthermore, the relevance of psychosocial or educational interventions to knowledge translation is also discussed as a source of knowledge. In summary, integrated knowledge translation is an important tool for the development of new interventions, as it helps to apply science to practice accurately. It supports the elaboration of the design while enhancing the relevance of the intervention through the validation of feasibility and acceptability with clinicians and patients.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.020 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".