Shared Decision Making in the Medical Encounter: Are We All Talking about the Same Thing?
Bibliographic record
Abstract
OBJECTIVE: This article aims to explore 1) whether after all the research done on shared decision making (SDM) in the medical encounter, a clear definition (or definitions) of SDM exists; 2) whether authors provide a definition of SDM when they use the term; 3) and whether authors are consistent, throughout a given paper, with respect to the research described and the definition they propose or cite. METHODS: The authors searched different databases (Medline, HealthStar, Cinahl, Cancerlit, Sociological Abstracts, and Econlit) from 1997 to December 2004. The keywords used were informed decision making and shared decision making as these are the keywords more often encountered in the literature. The languages selected were English and French. RESULTS: The 76 reported papers show that 1) several authors clearly define what they mean by SDM or by another closely related phrase, such as informed shared decision making. 2) About a third of the papers reviewed (25/76) cite these authors although 8 of them do not use the term in a manner consistent with the definition cited. 3) Certain authors use the term SDM inconsistently with the definition they propose, and some use the terms informed decision making and SDM as if they were synonymous. 4) Twenty-one papers do not provide or cite any definition, or their use of the term (i.e., SDM) is not consistent with the definition they provide. CONCLUSION: Although several clear definitions of shared decision making have been proposed, they are cited by only about a third of the papers reviewed. In the other papers, authors refer to the term without specifying or citing a definition or use the term inconsistently with their definition. This is a problem because having a clear definition of the concept and following this definition are essential to guide and focus research. Authors should use the term consistently with the identified definition.
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 | 0.086 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.026 | 0.038 |
| Open science | 0.004 | 0.014 |
| 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".