Bibliographic record
Abstract
Health services research (HSR) is commonly conceived as an applied discipline whose success is defined by its tangible impact on policy, practice or both. In Canada there has been a concerted effort to engage decision-makers in informing the research agenda. While it is admirable to aspire to practical utility, the HSR community has no control over the ultimate disposition of its work. Furthermore, the conditions for change must be present if the pathway from relevant, high-quality research to application is to be relatively smooth and immediate. In such cases, the changes may have occurred regardless of whether the research to support them took place. An examination of some widely renowned HSR reveals that timely and significant impact is relatively rare. Moreover, research that fundamentally changes how we view the world plays out over decades; it would be impossible to act on it in the short term, and in some cases it is not clear what ought to be done. The implications are that the first duty of HSR is to seek truth, and that funding and decision-making communities should define "useful" broadly, from a longer-term perspective. Taking the wide and the long view will in the end generate a greater return on investment in HSR than focusing too narrowly on contemporary preoccupations.
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 | 0.273 | 0.367 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.008 | 0.069 |
| Scholarly communication | 0.035 | 0.051 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.031 | 0.027 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".