Research priority setting for implementation science and practice: a living systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Research priority setting has the potential to bridge knowledge gaps, optimize resource allocation, foster collaborations, and inform funding directions for implementation science and practice when these priorities are properly acted upon. This systematic review aims to determine the extent of research in priority setting for implementation science and practice, examine the methodologies employed, synthesize these research priorities, and identify strategies for evaluating and implementing these priorities. METHODS: We will conduct a living systematic review following the Cochrane guidance. We will search literature from six databases, the website of James Lind Alliance, five implementation science-focused journals and several related journals, Google Scholar, and the reference lists of included studies. Two reviewers will independently screen studies based on the eligibility criteria. The characteristics of the included documents, their prioritization methods, and outcomes, as well as the evaluation and implementation strategies, will be extracted. We will critically appraise these documents using the nine common themes of good practice for research priority setting, and synthesize data using a narrative approach. We will re-run the search 12 months after the original search date to monitor the development of new literature and determine the time to update the review. DISCUSSIONS: By conducting this living systematic review, we will gain a comprehensive and dynamic understanding of the potential research gaps and hotspots in implementation science as perceived by researchers and practitioners. The findings of this review will inform the future research directions of implementation science and practice. SYSTEMATIC REVIEW REGISTRATION: This review has been registered with the Open Science Framework ( https://osf.io/sr69k ).
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 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 | 0.333 | 0.416 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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; both teacher heads 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".