Development of the ASSESS tool: a comprehenSive tool to Support rEporting and critical appraiSal of qualitative, quantitative, and mixed methods implementation reSearch outcomes
Bibliographic record
Abstract
BACKGROUND: Several tools to improve reporting of implementation studies for evidence-based decision making have been created; however, no tool for critical appraisal of implementation outcomes exists. Researchers, practitioners, and policy makers lack tools to support the concurrent synthesis and critical assessment of outcomes for implementation research. Our objectives were to develop a comprehensive tool to (1) describe studies focused on implementation that use qualitative, quantitative, and/or mixed methodologies and (2) assess risk of bias of implementation outcomes. METHODS: A hybrid consensus-building approach combining Delphi Group and Nominal Group techniques (NGT) was modeled after comparative methodologies for developing health research reporting guidelines and critical appraisal tools. First, an online modified NGT occurred among a small expert panel (n = 5), consisting of literature review, item generation, round robin with clarification, application of the tool to various study types, voting, and discussion. This was followed by a larger e-consensus meeting and modified Delphi process with implementers and implementation scientists (n = 32). New elements and elements of various existing tools, frameworks, and taxonomies were combined to produce the ASSESS tool. RESULTS: The 24-item tool is applicable to a broad range of study designs employed in implementation science, including qualitative studies, randomized-control trials, non-randomized quantitative studies, and mixed methods studies. Two key features are a section for assessing bias of the implementation outcomes and sections for describing the implementation strategy and intervention implemented. An accompanying explanation and elaboration document that identifies and describes each of the items, explains the rationale, and provides examples of reporting and appraising practice, as well as templates to allow synthesis of extracted data across studies and an instructional video, has been prepared. CONCLUSIONS: The comprehensive, adaptable tool to support both reporting and critical appraisal of implementation science studies including quantitative, qualitative, and mixed methods assessment of intervention and implementation outcomes has been developed. This tool can be applied to a methodologically diverse and growing body of implementation science literature to support reviews or meta-analyses that inform evidence-based decision-making regarding processes and strategies for implementation.
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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 |
|---|---|---|---|
| gemma | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.402 | 0.547 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.035 | 0.021 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
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 2 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".