Development and evaluation of shared decision-making tools in rheumatology: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Shared decision-making (SDM) tools are facilitators of decision-making through a collaborative process between patients/caregivers and clinicians. These tools help clinicians understand patient's perspectives and help patients in making informed decisions based on their preferences. Despite their usefulness for both patients and clinicians, SDM tools are not widely implemented in everyday practice. One barrier is the lack of clarity on the development and evaluation processes of these tools. Such processes have not been previously described in the field of rheumatology. OBJECTIVE: To describe the development and evaluation processes of shared decision-making (SDM) tools used in rheumatology. METHODS: Bibliographic databases (e.g., EMBASE and CINAHL) were searched for relevant articles. Guidelines for the PRISMA extension for scoping reviews were followed. Studies included were: addressing SDM among adults in rheumatology, focusing on development and/or evaluation of SDM tool, full texts, empirical research, and in the English language. RESULTS: Of the 2030 records screened, forty-six reports addressing 36 SDM tools were included. Development basis and evaluation measures varied across the studies. The most commonly reported development basis was the International Patient Decision Aids Standards (IPDAS) criteria (19/36, 53 %). Other developmental foundations reported were: The Ottawa Decision Support Framework (ODSF) (6/36, 16 %), Informed Medical Decision Foundation elements (3/36, 8 %), edutainment principles (2/36, 5.5 %), and others (e.g. DISCERN and MARKOV Model) (9/31,29 %). The most commonly used evaluation measures were the Decisional Conflict Scale (18/46, 39 %), acceptability and knowledge (7/46, 15 %), and the preparation for decision-making scale (5/46,11 %). CONCLUSION: For better quality and wider implementation of such tools, there is a need for detailed, transparent, systematic, and consistent reporting of development methods and evaluation measures. Using established checklists for reporting development and evaluation is encouraged.
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 | 0.120 | 0.307 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.037 | 0.035 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| 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".