The SPARK Tool to prioritise questions for systematic reviews in health policy and systems research: development and initial validation
Bibliographic record
Abstract
BACKGROUND: Groups or institutions funding or conducting systematic reviews in health policy and systems research (HPSR) should prioritise topics according to the needs of policymakers and stakeholders. The aim of this study was to develop and validate a tool to prioritise questions for systematic reviews in HPSR. METHODS: We developed the tool following a four-step approach consisting of (1) the definition of the purpose and scope of tool, (2) item generation and reduction, (3) testing for content and face validity, (4) and pilot testing of the tool. The research team involved international experts in HPSR, systematic review methodology and tool development, led by the Center for Systematic Reviews on Health Policy and Systems Research (SPARK). We followed an inclusive approach in determining the final selection of items to allow customisation to the user's needs. RESULTS: The purpose of the SPARK tool was to prioritise questions in HPSR in order to address them in systematic reviews. In the item generation and reduction phase, an extensive literature search yielded 40 relevant articles, which were reviewed by the research team to create a preliminary list of 19 candidate items for inclusion in the tool. As part of testing for content and face validity, input from international experts led to the refining, changing, merging and addition of new items, and to organisation of the tool into two modules. Following pilot testing, we finalised the tool, with 22 items organised in two modules - the first module including 13 items to be rated by policymakers and stakeholders, and the second including 9 items to be rated by systematic review teams. Users can customise the tool to their needs, by omitting items that may not be applicable to their settings. We also developed a user manual that provides guidance on how to use the SPARK tool, along with signaling questions. CONCLUSION: We have developed and conducted initial validation of the SPARK tool to prioritise questions for systematic reviews in HPSR, along with a user manual. By aligning systematic review production to policy priorities, the tool will help support evidence-informed policymaking and reduce research waste. We invite others to contribute with additional real-life implementation of the tool.
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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: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | Metaresearch Domain: Methods · 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 | 0.378 | 0.595 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.034 | 0.025 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".