Public Spending on Health Services and Policy Research in Canada: A Reflection on Thakkar and Sullivan Comment on "Public Spending on Health Service and Policy Research in Canada, the United Kingdom, and the United States: A Modest Proposal"
Bibliographic record
Abstract
Vidhi Thakkar and Terrence Sullivan have done a careful and thought-provoking job in trying to establish comparable estimates of public spending on health services and policy research (HSPR) in Canada, the United Kingdom and the United States. Their main recommendation is a call for an international collaboration to develop common terms and categories of HSPR. This paper raises two additional questions that have an international comparative dimension: There is little doubt that public spending on HSPR represents more than the "tip of the iceberg," but how much more? And how do the countries fare on the uptake of HSPR by decision-makers? I have long speculated that probably as much or more is spent by provincial/territorial governments, regional health authorities, hospitals and other agencies on HSPR activities carried out by consultants in Canada than by the federal, provincial/territorial granting agencies. Support for this contention is provided in a paper by Penno and Gauld on spending on external consultancies by New Zealand's District Health Boards (DHBs). Their estimate of the amount spent on consultancies in 2014/15 represents 80% of the amount spent on research by the Health Research Council of New Zealand in 2015. In terms of the uptake of research Jonathan Lomas pioneered the concept of linking researchers with decisionmakers when he became the founding Chief Executive Officer (CEO) of the Canadian Health Services Research Foundation (CHSRF) in 1997. An early assessment was promising, and it would be interesting to know if other countries have tried this. Most assessments of research uptake and impact are short-term in nature. It might be insightful to assess HSPR developments over the long term, such as prospective reimbursement through diagnosis related groups (DRGs) that has been evolving internationally for more 40+ years. In the short term the prospects for a major infusion of funding in HSPR in Canada are not promising, although there have been welcome investments in the Canadian Foundation for Healthcare Improvement (formerly CHSRF).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.033 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.030 | 0.031 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.037 | 0.046 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".