A conceptual framework for patient-directed knowledge tools to support patient-centred care: Results from an evidence-informed consensus meeting
Bibliographic record
Abstract
OBJECTIVE: Patient-directed knowledge tools are designed to engage patients in dialogue or deliberation, to support patient decision-making or self-care of chronic conditions. However, an abundance of these exists. The tools themselves and their purposes are not always clearly defined; creating challenges for developers and users (professionals, patients). The study's aim was to develop a conceptual framework of patient-directed knowledge tool types. METHODS: A face-to-face evidence-informed consensus meeting with 15 international experts. After the meeting, the framework went through two rounds of feedback before informal consensus was reached. RESULTS: A conceptual framework containing five patient-directed knowledge tool types was developed. The first part of the framework describes the tools' purposes and the second focuses on the tools' core elements. CONCLUSION: The framework provides clarity on which types of patient-directed tools exist, the purposes they serve, and which core elements they prototypically include. It is a working framework and will require further refinement as the area develops, alongside validation with a broader group of stakeholders. PRACTICE IMPLICATIONS: The framework assists developers and users to know which type a tool belongs, its purpose and core elements, helping them to develop and use the right tool for the right job.
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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 | 0.334 | 0.226 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.011 | 0.023 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".