Tools for assessing health research partnership outcomes and impacts: a systematic review
Bibliographic record
Abstract
Abstract Objective To identify and assess the globally available valid, reliable and acceptable tools for assessing health research partnership outcomes and impacts. Methods We searched Ovid MEDLINE, Embase, CINAHL Plus and PsycINFO from origin to 2 June 2021, without limits, using an a priori strategy and registered protocol. We screened citations independently and in duplicate, resolving discrepancies by consensus and retaining studies involving health research partnerships, the development, use and/or assessment of tools to evaluate partnership outcomes and impacts, and reporting empirical psychometric evidence. Study, tool, psychometric and pragmatic characteristics were abstracted using a hybrid approach, then synthesized using descriptive statistics and thematic analysis. Study quality was assessed using the quality of survey studies in psychology (Q-SSP) checklist. Results From 56 123 total citations, we screened 36 027 citations, assessed 2784 full-text papers, abstracted data from 48 studies and one companion report, and identified 58 tools. Most tools comprised surveys, questionnaires and scales. Studies used cross-sectional or mixed-method/embedded survey designs and employed quantitative and mixed methods. Both studies and tools were conceptually well grounded, focusing mainly on outcomes, then process, and less frequently on impact measurement. Multiple forms of empirical validity and reliability evidence was present for most tools; however, psychometric characteristics were inconsistently assessed and reported. We identified a subset of studies (22) and accompanying tools distinguished by their empirical psychometric, pragmatic and study quality characteristics. While our review demonstrated psychometric and pragmatic improvements over previous reviews, challenges related to health research partnership assessment and the nascency of partnership science persist. Conclusion This systematic review identified multiple tools demonstrating empirical psychometric evidence, pragmatic strength and moderate study quality. Increased attention to psychometric and pragmatic requirements in tool development, testing and reporting is key to advancing health research partnership assessment and partnership science. PROSPERO CRD42021137932
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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 | 0.079 | 0.233 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.050 | 0.041 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".