Knowledge exchange sessions on primary health care research findings in public libraries: A qualitative study with citizens in Quebec
Bibliographic record
Abstract
Little is known about knowledge transfer with the public. We explored how citizens, physicians, and communication specialists understand knowledge transfer in public spaces such as libraries. The initial study aimed at evaluating the scaling up of a program on disseminating research findings on potentially inappropriate medication. Twenty-two citizen workshops were offered by 16 physicians and facilitated by 6 communication specialists to 322 citizens in libraries during spring 2019. We did secondary analysis using the recorded workshop discussions to explore the type of knowledge participants used. Participants described four kinds of knowledge: biomedical, sociocultural beliefs, value-based reasoning, and institutional knowledge. Biomedical knowledge included scientific evidence, research methods, clinical guidelines, and access to research outcomes. Participants discussed beliefs in scientific progress, innovative clinical practices, and doctors' behaviours. Participants discussed values related to reliability, transparency, respect for patient autonomy and participation in decision-making. All categories of participants used these four kinds of knowledge. However, their descriptions varied particularly for biomedical knowledge which was described by physician-speakers and communication specialists-facilitators as scientific evidence, epidemiological and clinical practice guidelines, and pathophysiological theories. Communication specialists-facilitators also described scientific journalistic sources and scientific journalistic reports as proxies of scientific evidence. Citizens described biomedical knowledge in terms of knowledge to make informed decisions. These findings offer insights for future scientific knowledge exchange interventions with the public.
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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 | 0.030 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".