Using variation between countries to estimate demand for Cochrane reviews when access is free: a cost–benefit analysis
Bibliographic record
Abstract
OBJECTIVES: Cochrane reviews are currently of limited use as many healthcare professionals and patients have no access to them. Most member states of the Organisation for Economic Co-operation and Development (OECD) choose not to pay for nationwide access to the reviews, possibly uncertain whether there is enough demand to warrant the costs of a national subscription. This study estimates the demand for review downloads and summary views under free access across all OECD countries. DESIGN: The study employs a retrospective design in analysing observational data of web traffic to Cochrane websites in 2014. Specifically, we model for each country downloads of Cochrane reviews and views of online summaries as a function of free access status and alternative sources of variation across countries. The model is then used to estimate demand if a country with restricted access were to purchase free access. We use these estimates to perform a cost-benefit analysis. RESULTS: For one group of eight OECD countries, the additional downloads under free access are estimated to cost between US$4 and more than US$20 each. Three countries are expected to save money under free access, as existing institutional subscriptions would no longer be needed. For the largest group of 17 member states, free access is estimated to cost US$0.05-US$2 per additional review download. On average, the increase in review downloads does not appear to be associated with a decrease in the number of summary views. Instead, translations of plain-language summaries into national languages can serve as an additional strategy for dissemination. CONCLUSIONS: We estimate that free access would cost less than US$2 per additional download for 20 of the 28 OECD countries without national subscriptions, including Canada, Germany and Israel. These countries may be encouraged by our findings to provide free access to their citizens.
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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 | 0.072 | 0.321 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.019 |
| Bibliometrics | 0.012 | 0.020 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".