Measurement Issues in Dissemination and Implementation Research
Bibliographic record
Abstract
Abstract The National Institutes of Health, the Agency for Healthcare Research and Quality, the Centers for Disease Control and Prevention, and a number of private foundations have expressed the need for advancing the science of dissemination and implementation (D&I). Interest in D&I research is present in many countries, including the United Kingdom (UK Center for Reviews and Dissemination, the UK Medical Research Council) and Canada (Canadian Institutes of Health Research). Improving healthcare requires not only effective programs and interventions but also effective strategies to move them into community-based settings of care. But before discrete strategies can be tested for effectiveness, comparative effectiveness, or cost-effectiveness, context, and outcome constructs must be identified and defined in such a way that enables their manipulation and measurement. Measurement is underdeveloped with few psychometrically strong measures and very little attention paid to their pragmatic nature. A variety of tools is needed to capture healthcare access and quality, and no measurement issues are more pressing than those for D&I science.
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 | 0.757 | 0.860 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.027 | 0.034 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".