Using the Nine Common Themes of Good Practice checklist as a tool for evaluating the research priority setting process of a provincial research and program evaluation program
Bibliographic record
Abstract
BACKGROUND: Given the context-specific nature of health research prioritization and the obligation to effectively allocate resources to initiatives that will achieve the greatest impact, evaluation of priority setting processes can refine and strengthen such exercises and their outcomes. However, guidance is needed on evaluation tools that can be applied to research priority setting. This paper describes the adaption and application of a conceptual framework to evaluate a research priority setting exercise operating within the public health sector in Ontario, Canada. METHODS: The Nine Common Themes of Good Practice checklist, described by Viergever et al. (Health Res Policy Syst 8:36, 2010) was used as the conceptual framework to evaluate the research priority setting process developed for the Locally Driven Collaborative Projects (LDCP) program in Ontario, Canada. Multiple data sources were used to inform the evaluation, including a review of selected priority setting approaches, surveys with priority setting participants, document review, and consultation with the program advisory committee. RESULTS: The evaluation assisted in identifying improvements to six elements of the LDCP priority setting process. The modifications were aimed at improving inclusiveness, information gathering practices, planning for project implementation, and evaluation. In addition, the findings identified that the timing of priority setting activities and level of control over the process were key factors that influenced the ability to effectively implement changes. CONCLUSIONS: The findings demonstrate the novel adaptation and application of the 'Nine Common Themes of Good Practice checklist' as a tool for evaluating a research priority setting exercise. The tool can guide the development of evaluation questions and enables the assessment of key constructs related to the design and delivery of a research priority setting process.
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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: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.405 | 0.437 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".