Information Seeking Experiences of Canadian Pharmaceutical Policy Makers
Bibliographic record
Abstract
Background Research-informed public policy is often articulated as an ideal. Yet, “evidence-based policy making” has also been critiqued for not fully taking into account the context in which policy makers actually work. This exploratory study investigates the work-related information seeking experiences of key informants engaged in pharmaceutical policy making in Canada. Methods As part of a broader research prioritysetting process, we conducted semi-structured interviews with a purposive sample of 15 Canadian pharmaceutical policy decision makers. Interviews were audio-recorded, transcribed and coded using NVivo 8. We used descriptive qualitative analysis influenced by grounded theory methods We compared our results with Leckie, Pettigrew & Sylvain’s General Model of Information Seeking of Professionals to create a model specific to our study population. Pharmaceutical policy makers need information for their work, and their information seeking is not dissimilar to that of other professionals. Results Approaches to seeking were diverse, and may reflect a status hierarchy in which access to resources is unequally distributed. Sources used also appeared to indicate levels of status. Affective outcomes were commonly disappointment, desire for a single go-to source, and resignation to making do without evidence. Time pressures were a concern across respondents, and influenced seeking actions as well as outcomes. Conclusions Specific types and time-sensitivity of needs, as well as a lack of established sources, create affective outcomes that point to areas of improvement for information sharing and knowledge translation. In the absence of a dedicated, independent source for rapid-response policy research, Canadian pharmaceutical policy makers will continue to satisfice with available resources, and barriers to evidence-informed policy will persist.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.037 | 0.011 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".