Can patients be trained to expect shared decision making in clinical consultations? Feasibility study of a public library program to raise patient awareness
Bibliographic record
Abstract
INTRODUCTION: Shared decision making (SDM) is a process whereby decisions are made together by patients and/or families and clinicians. Nevertheless, few patients are aware of its proven benefits. This study investigated the feasibility, acceptability and impact of an intervention to raise public awareness of SDM in public libraries. MATERIALS AND METHODS: A 1.5 hour interactive workshop to be presented in public libraries was co-designed with Quebec City public library network officials, a science communication specialist and physicians. A clinical topic of maximum reach was chosen: antibiotic overuse in treatment of acute respiratory tract infections. The workshop content was designed and a format, whereby a physician presents the information and the science communication specialist invites questions and participation, was devised. The event was advertised to the general public. An evaluation form was used to collect data on participants' sociodemographics, feasibility and acceptability components and assess a potential impact of the intervention. Facilitators held a post-workshop focus group to qualitatively assess feasibility, acceptability and impact. RESULTS: All 10 planned workshops were held. Out of 106 eligible public participants, 89 were included in the analysis. Most participants were women (77.6%), retired (46.1%) and over 45 (59.5%). Over 90% of participants considered the workshop content to be relevant, accessible, and clear. They reported substantial average knowledge gain about antibiotics (2.4, 95% Confidence Interval (CI): 2.0-2.8; P < .001) and about SDM (4.0, 95% CI: 3.4-4.5; P < .001). Self-reported knowledge gain about SDM was significantly higher than about antibiotics (4.0 versus 2.4; P < .001). Knowledge gain did not vary by sociodemographic characteristics. The focus group confirmed feasibility and suggested improvements. CONCLUSIONS: A public library intervention is feasible and effective way to increase public awareness of SDM and could be a new approach to implementing SDM by preparing potential patients to ask for it in the consulting room.
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 | 0.022 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".