Measurement resources for dissemination and implementation research in health
Bibliographic record
Abstract
BACKGROUND: A 2-day consensus working meeting, hosted by the United States National Institutes of Health and the Veterans Administration, focused on issues related to dissemination and implementation (D&I) research in measurement and reporting. Meeting participants included 23 researchers, practitioners, and decision makers from the USA and Canada who concluded that the field would greatly benefit from measurement resources to enhance the ease, harmonization, and rigor of D&I evaluation efforts. This paper describes the findings from an environmental scan and literature review of resources for D&I measures. FINDINGS: We identified a total of 17 resources, including four web-based repositories and 12 static reviews or tools that attempted to synthesize and evaluate existing measures for D&I research. Thirteen resources came from the health discipline, and 11 were populated from database reviews. Ten focused on quantitative measures, and all were generated as a resource for researchers. Fourteen were organized according to an established D&I theory or framework, with the number of constructs and measures ranging from 1 to more than 450. Measure metadata was quite variable with only six providing information on the psychometric properties of measures. CONCLUSIONS: Additional guidance on the development and use of measures are needed. A number of approaches, resources, and critical areas for future work are discussed. Researchers and stakeholders are encouraged to take advantage of a number of funding mechanisms supporting this type of work.
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
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.571 | 0.762 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.045 | 0.055 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.009 | 0.028 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.046 | 0.016 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".