The pain funding gap: A database analysis of pain research funding in Canada from 2008–2023
Bibliographic record
Abstract
Background One in five Canadians experiences chronic pain, at a cost of $40.3 billion in 2019. Despite this significant burden, there are few effective treatments for pain. This gap has been recognized by Health Canada, which has put forth the Action Plan for Pain in Canada. Advancing our understanding of pain mechanisms and clinical trials to identify novel therapeutics are essential to address this treatment gap. However, it remains unknown whether the recommendations of the Action Plan have increased research investments.Methods We investigate research investments in pain by the Canadian Institutes of Health Research (CIHR) based on publicly available data. We performed a systematic database search focused on operating funds from competitions between 2008 and 2023 and tabulated pain funding as a proportion of total CIHR operational funds granted each year. Next, we examined the proportion of pain funding across CIHR institutes aggregated across funding years.Results We identified 20,126 operational grants, of which 459 were pain focused. The highest level of pain funding was 3.32% in 2019, and the average (SD) was 2.13% (0.70%). Funding was stagnant from 2008 to 2023 (R2 = 0.10, P = 0.23). The Institute of Musculoskeletal Health and Arthritis allocated the largest proportion of funding to pain research (11.40%). Eight of the 13 institutes allocated less than 1% of their operating funds to pain research.Interpretation In sum, CIHR pain research funding does not match the socioeconomic burden posed by pain. We propose three action items to improve pain research funding and to ultimately relieve the burden of pain in Canada.
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How this classification was reachedexpand
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 | 0.090 | 0.305 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.001 | 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, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".