Characterizing Canadian funded partnered health research projects between 2011 and 2019: a retrospective analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Involving research users in collaborative research approaches may increase the relevance and utility of research findings. Our primary objectives were to (i) identify and describe characteristics of Canadian federally and provincially funded health research projects that included research users and were funded between 2011 and 2019; (ii) explore changes over time; and (iii) compare characteristics between funder required and optional partnerships. METHODS: Retrospective analysis. Inclusion criteria were projects that included research users. We analyzed publicly available project variables, and coded field and type of research using established classification systems. We summarized data with descriptive statistics and compared variables across three funding year blocks and partnership requirement status. RESULTS: We identified 1153 partnered health research projects, representing 137 fields of research and 37 types of research categories. Most projects included a required partnership (80%) and fell into health and social care services research (66%). Project length and funding amount increased from average of 24.8 months and $266 248 CAD in 2011-2013 to 31.6 months and $438 766 CAD in 2017-2019. There were significantly fewer required partnerships in 2017-2019. CONCLUSIONS: Between 2011 and 2019 Canadian federally and provincially funded partnered health research reflected primarily care services research across many fields. The observed breadth suggests that partnered health research approaches are applicable in many fields of research. Additional work to support partnered research across all types of health research (especially biomedical research) is warranted. The administration of larger grants that are funded for longer time periods may address previously identified concerns among research teams engaging in partnered research but may mean that fewer teams receive funding and risk delaying responding to time-sensitive data needs for users. Our process and findings can be used as a starting point for international comparison.
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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 | MetaresearchBibliometrics Domain: Methods · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | medium |
| gpt | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | high |
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 | 0.107 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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".