The use of academic research in public health policy and practice
Bibliographic record
Abstract
This study sought to gain a better understanding of the ways in which users access academic research, by observing decision-making at the micro level in a public health unit (PHU) in Ontario, Canada. The overarching question guiding the study is as follows: how do PHU staff members access, engage with, and make use of academic research in order to advance their mandate? Ethnographic methods were used to collect data from direct observations and informant input, augmented by document review. A two-dimensional (2D) continuum of research use was adopted as an organizing heuristic. Research use was shown to be highly dynamic, spanning (spatially) across and transitioning (temporally) through both dimensions of the 2D organizing heuristic. While this research focuses on the context of use, it acknowledges interactions with the other contexts. This study suggests that users may have more ‘agency’ in the ways in which they engage with and use research. The full range of possibilities discussed is critical for accurately documenting the impacts of academic research. Findings should be relevant to other sectors where research use capacity is being developed.
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 | 0.234 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.025 | 0.068 |
| Scholarly communication | 0.043 | 0.020 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".