Scaling Up Citizen Workshops in Public Libraries to Disseminate and Discuss Primary Care Research Results: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: Little is known about engaging patients and stakeholders in the process of scaling up effective knowledge translation interventions targeting the public. OBJECTIVE: Using an integrated knowledge translation approach, we aimed to scale up and evaluate an effective pilot program to disseminate research results in public libraries. METHODS: We conducted a scaling-up study targeting the public. On the basis of our successful pilot project, we codeveloped and implemented a large-scale program of free citizen workshops in public libraries, in a close research partnership with stakeholders and patient representatives. Citizen workshops, each facilitated by 1 participating physician and 1 science communicator, consisted of a 45-minute computer-assisted presentation and a 45-minute open exchange. The intervention outcome was knowledge gained. The scale-up outcomes were satisfaction, appropriateness, coverage, and costs. An evaluation questionnaire was used to collect data of interest. Both quantitative and qualitative analyses were performed. RESULTS: The workshop theme chosen by the patient and stakeholder representatives was the high prevalence of medication overuse among people aged ≥65 years. From April to May 2019, 26 workshops were conducted in 25 public libraries reaching 362 people. The mean age of participants was 64.8 (SD 12.5) years. In total, 18 participating physicians and 6 science communicators facilitated the workshops. Participants reported significant knowledge gain (mean difference 2.1, 95% CI 2.0-2.2; P<.001). The median score for overall public satisfaction was 9 out of 10 (IQR 8-10). The public participants globally rated the workshops as having a high level of appropriateness. Coverage was 92% (25/27) of the total number of public libraries targeted. Costs were CAD $6051.84 (US $4519.69) for workshop design and CAD $22,935.41 (US $17,128.85) for scaling them up. CONCLUSIONS: This project successfully established a large-scale and successful implementation science or knowledge translation bridge among researchers, clinicians, and citizens via public libraries. This study provides a model for a dissemination practice that benefits the public by both engaging them in the dissemination process and targeting them directly.
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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 | 0.044 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".