Consensus-Based Development of an Assessment Tool: A Methodology for Patient Engagement in Primary Care and CPD Research
Bibliographic record
Abstract
ABSTRACT: With cardiovascular disease (CVD) posing a significant disease burden in Canada and more broadly, preventative efforts which incorporate best evidence, patient preference, and physician expertise must continue to take place. Primary care providers play a pivotal role in this effort, and a greater understanding of patient perspectives is needed to guide management and inform training. We used a validated consensus method, the nominal group technique (NGT), to identify patient-reported experience measures (PREM) related to CVD prevention deemed most important by both patients and providers. The NGT was used by using structured discussions between patients and providers to bring ideas about PREM CVD outcomes to a consensus. Four patient partners and four primary care providers were selected to participate in an NGT session. Each participant wrote down items/questions they believed important in CVD preventative care. After discussions, all items underwent anonymous ranking on a 5-point scale. Items were included/excluded based on 75% agreement a priori. The panel produced 10 items from a total of 26 after 2 rounds of ranking. The top two items were as follows: "Is your treatment plan tailored to you" and "Was your physician good at giving information about your risk factors?" These results are significantly different compared with existing quality measures because they highlight aspects of patient experience and therapeutic relationship. A questionnaire consisting of prioritized PREM items is valuable in quality improvement and continuous professional development (CPD).
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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 | 0.305 | 0.424 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".