Arts-Based Approaches to Priority Setting: Current Applications and Future Possibilities
Bibliographic record
Abstract
Background: Arts-based research methods and priority setting research both reflect growing commitments towards public and participant involvement in research activities. This has contributed to a growth of these respective methods across research disciplines and sectors, but their intersection has not been explored. Methods: We conducted a scoping review to map the state-of-the-science of arts-based approaches within priority setting research. We conducted an open search of three databases, conducted reference list mining, and hand-searched two journals to identify relevant articles. Of 5457 records retrieved, 11 met our pre-established inclusion criteria and were included in the review. Data were extracted using Microsoft Excel to produce narrative and descriptive summaries. Findings: All articles had some degree of health focus. Areas of priority setting centered on chronic illness, hard to reach populations, cancer survivorship, dental reform, and youth. In terms of artistic approaches, the majority of the articles included visual methods, with some articles utilizing multiple art approaches such as music, drawing, and filmmaking. Qualitative methods were used in all articles alongside the arts-based approaches, encompassing techniques such as interviews, discussions, and storytelling. Priority setting activites were mainly facilitated in group settings. The suggested benefits of arts-based approaches included enhancing participant communication, improving accessible research dissemination, and encouraging dialogues on identified health concerns. Challenges with using arts-based approaches included limitations on time and resources. Conclusion: Arts-based approaches to priority setting is an expanding field, with clear applications across various research contexts and priority focal areas. Further attention to the integration of arts-based approaches within priority setting, their theoretical underpinnings, and concurrent development and evaluation of arts-based priority setting methods are warranted.
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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 | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.021 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".