The Society for Implementation Research Collaboration Instrument Review Project: A methodology to promote rigorous evaluation
Bibliographic record
Abstract
BACKGROUND: Identification of psychometrically strong instruments for the field of implementation science is a high priority underscored in a recent National Institutes of Health working meeting (October 2013). Existing instrument reviews are limited in scope, methods, and findings. The Society for Implementation Research Collaboration Instrument Review Project's objectives address these limitations by identifying and applying a unique methodology to conduct a systematic and comprehensive review of quantitative instruments assessing constructs delineated in two of the field's most widely used frameworks, adopt a systematic search process (using standard search strings), and engage an international team of experts to assess the full range of psychometric criteria (reliability, construct and criterion validity). Although this work focuses on implementation of psychosocial interventions in mental health and health-care settings, the methodology and results will likely be useful across a broad spectrum of settings. This effort has culminated in a centralized online open-access repository of instruments depicting graphical head-to-head comparisons of their psychometric properties. This article describes the methodology and preliminary outcomes. METHODS: The seven stages of the review, synthesis, and evaluation methodology include (1) setting the scope for the review, (2) identifying frameworks to organize and complete the review, (3) generating a search protocol for the literature review of constructs, (4) literature review of specific instruments, (5) development of an evidence-based assessment rating criteria, (6) data extraction and rating instrument quality by a task force of implementation experts to inform knowledge synthesis, and (7) the creation of a website repository. RESULTS: To date, this multi-faceted and collaborative search and synthesis methodology has identified over 420 instruments related to 34 constructs (total 48 including subconstructs) that are relevant to implementation science. Despite numerous constructs having greater than 20 available instruments, which implies saturation, preliminary results suggest that few instruments stem from gold standard development procedures. We anticipate identifying few high-quality, psychometrically sound instruments once our evidence-based assessment rating criteria have been applied. CONCLUSIONS: The results of this methodology may enhance the rigor of implementation science evaluations by systematically facilitating access to psychometrically validated instruments and identifying where further instrument development is needed.
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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 | 0.712 | 0.769 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.038 | 0.038 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.009 | 0.028 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.025 | 0.015 |
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".