Supporting effective participation in health guideline development groups: The Guideline Participant Tool
Bibliographic record
Abstract
OBJECTIVES: Health guidelines are a key knowledge translation tool produced and used by numerous stakeholders worldwide. Effective participation in guideline development groups or development groups is crucial for guideline success, yet little guidance exists for members of these groups. In this study, we present the Guideline Participant Tool (GPT) to support effective participation in guideline groups, in particular those using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. STUDY DESIGN AND SETTING: We used a mixed methods and iterative approach to develop a tool to support guideline participation. We used the findings of a published systematic review to develop an initial list of items for considerations for guideline participants. Then, we refined this list through key informant interviews with guideline chairs, sponsors, and participants. Finally, we validated the GPT in three guideline groups with 26 guideline group members. RESULTS: The initial list of items based on 37 articles from the existing systematic review included 15 themes and 61 items for a draft tool. Ten key informant interviews helped us refine the list to include the following themes: selection of participants, guideline group process, and tool format. 26 respondents completed the validation survey from three guideline groups. Refinement of the tool ultimately generated a GPT with 33 items for participant consideration before, during, and in follow-up to guideline group meetings. CONCLUSION: The GPT contains helpful guidance for all guideline participants, particularly those without previous guideline experience. Future research should further explore the need for additional tools to support guideline participants and identify and develop strategies for improving guideline members' participation in guideline groups. This work will be incorporated into INGUIDE.org guideline training and credentialing efforts by the Guidelines International Network and McMaster University.
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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: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.129 | 0.544 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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, 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".