Identifying candidate quality indicators of tools that support the practice of knowledge translation: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this scoping review is to identify and characterize relevant knowledge translation methods tools (those that provide guidance for optimized knowledge translation practice) to uncover candidate quality indicators to inform a future quality assessment tool for knowledge translation strategies. INTRODUCTION: Knowledge translation strategies (defined as including knowledge translation interventions, tools, and products) target various knowledge users, including patients, clinicians, researchers, and policy-makers. The development and use of strategies that support knowledge translation practice have been rapidly increasing, making it difficult for knowledge users to decide which to use. There is limited evidence-based guidance or measures to help assess the overall quality of knowledge translation strategies. INCLUSION CRITERIA: Empirical and non-empirical documents will be considered if they explicitly describe a knowledge translation methods tool and its development, evaluation or validation, methodological strengths or limitations, and/or use over time. The review will consider a knowledge translation methods tool if it falls within at least one knowledge translation domain (ie, implementation, dissemination, sustainability, scalability, integrated knowledge translation) in the health field. METHODS: We will conduct a systematic search of relevant electronic databases and gray literature. The search strategy will be developed iteratively by an experienced medical information specialist and peer-reviewed with the PRESS checklist. The search will be limited to English-only documents published from 2005 onward. Documents will be independently screened, selected, and extracted by 2 researchers. Data will be analyzed and summarized descriptively, including the characteristics of the included documents, knowledge translation methods tools, and candidate quality indicators. SCOPING REVIEW REGISTRATION: Open Science Framework ( https://osf.io/chxvq ).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| grok | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
| opus | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | medium |
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 | 0.047 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 3 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".