Open access archiving and article citations within health services and policy research
Bibliographic record
Abstract
Objective: The Canadian Institutes of Health Research (CIHR) is now among the many funders that require grant recipients to make research outputs Open Access (OA). This study describes OA-archiving practises within journals containing Canadian health services and policy research, and examines the association between OA status and citations to an article. Methods: We employed an article-level analysis comparing citation rates for articles drawn from the same, purposively selected journals. We used descriptive statistics to describe archiving practises, and a two-stage analytic approach designed to test whether OA is associated with likelihood that an article is cited at all and total number citations that an article receives, conditional on being cited at least once. Results: Adjusting for several potential confounders, OA archived articles were 60% more likely to be cited at least once, and, once cited, were cited 29% more than non-OA articles. The majority of articles that were made OA were archived in just one location, and archived copies found were primarily on websites rather than institutional or subject-based repositories.Discussion: It appears that there may be a citation “advantage” associated with articles made open access in this field. Whether this advantage is solely a result of OA status cannot be confirmed from this data alone. Regarding archiving practises, it is concerning that the vast majority of archived copies are on web sites, as findability and preservation of these archived copies may be inferior to those in centralized institutional or subject-based repositories.
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 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 | 0.088 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.003 | 0.001 |
| 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, 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".