Engaging knowledge users in a systematic review on the comparative effectiveness of geriatrician-led models of care is possible: A cross-sectional survey using the Patient Engagement Evaluation Tool
Bibliographic record
Abstract
BACKGROUND: A systematic review (SR) was conducted to evaluate the comparative effectiveness of geriatrician-led models of care, and an integrated knowledge translation (iKT) approach facilitated SR relevance. Activities to engage knowledge users (KUs) in the SR were evaluated for perceived level of engagement. STUDY DESIGN AND SETTING: KUs included patients, caregivers, geriatricians, and policymakers from three Canadian provinces. Activities included 1) modified Delphi to select outcomes; 2) cross-sectional survey to select outcome measures, and 3) in-person meeting to discuss SR findings. KU engagement was assessed using the Patient Engagement Evaluation Tool (PEET) after the second and third activities. KUs rated the extent of successful engagement using a 7-point Likert scale ranging from "no extent" to "very large extent." RESULTS: In total, 15 KUs completed the PEET: eight geriatricians, four policymakers, two patients, and one caregiver. Median engagement scores across all activities (median range: 6.00-6.50) indicated that KUs felt engaged. Differences were observed for activity type; perceived engagement at in-person meeting resulted in higher meta-criteria scores for trust (P = 0.005), legitimacy (P = 0.003), fairness (P = 0.013), and competency (P = 0.035) compared with online activities. CONCLUSIONS: KUs can be engaged meaningfully in SR processes. Their perceived engagement was higher for in-person than for online activities.
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 | 0.229 | 0.460 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".