Information behaviour of Canadian pharmaceutical policy makers
Bibliographic record
Abstract
OBJECTIVES: Understanding the information behaviour of policy makers targeted by knowledge translation efforts is key to improving policy research impact. This study explores the reported information behaviour of pharmaceutical policy decision-makers in Canada, a country highly associated with evidence-based practice yet still facing substantial barriers to evidence-informed health policy. METHODS: We conducted semi-structured telephone interviews with a purposive sample of 15 Canadian pharmaceutical policy decision-makers. Results of the descriptive, qualitative analysis were compared with the General Model of Information Seeking of Professionals (GMISP) proposed by Leckie, Pettigrew and Sylvain in 1996. RESULTS: Characteristics of information needs included topic, depth/breadth of questions and time sensitivity. Approaches to information seeking were variously scattershot, systematic and delegated, depending on the characteristics as well as respondent resources. Major source types were human experts, electronic sources and trusted organisations. Affective (emotion-related) outcomes were common, including frustration and desire for better information systems and sources. CONCLUSIONS: The GMISP model may be adapted to model information behaviour of Canadian pharmaceutical policy makers. In the absence of a dedicated, independent source for rapid-response policy research, these policy makers will likely continue to satisfice (make do) with available resources, and barriers to evidence-informed policy will persist.
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 | 0.019 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".