Envisioning Shared Decision Making: A Reflection for the Next Decade
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the evolving evidence in favor of shared decision making (SDM) and of decades-long calls for its adoption, SDM remains uncommon in routine care. Reflecting on this lack of progress, we sought to reimagine the future of SDM and the path to take us there. In late 2017, a multidisciplinary and international group of six researchers were challenged by a senior SDM scholar to envision the future and, based on a provocatively critical view of the present, to write letters to themselves from the year 2028. Letters were exchanged and discussed electronically. The group then met in person to discuss the letters. Since the letters painted a dystopian picture, they triggered questions about the nature of SDM, who should benefit from SDM, how to measure its contribution to care, and what new ways can be invented to design and test interventions to implement SDM in routine care. Through contrasting the purposefully generated dystopias with an ideal future for SDM, we generated reflections on a research agenda for SDM. These reflections hinged on recognizing SDM's contributing to care, that is, as a way to advance the problematic human situation of patients. These focused on three distinct yet complimentary contributors to SDM: 1) the process of making decisions, 2) humanistic communication, and 3) fit-to-care of the resulting decision. The group then concluded that to move SDM from envisioned to routine practice, and to ensure it reaches all, particularly persons rendered vulnerable by current forms of health care, a substantial investment in implementation research is necessary. Perhaps the discussion of these reflections can contribute to a path forward that will improve the likelihood of the future we dream for SDM.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.279 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it