Shared decision-making in palliative care: A systematic mixed studies review using narrative synthesis
Bibliographic record
Abstract
The aim of this study is to synthesize knowledge about the process of shared decision-making (SDM) in palliative care. Medline, EMBASE, CINAHL, PsychInfo, Web of Science were searched with core concepts: shared decisions, patient participation in decision-making, and palliative care. Titles and abstracts were screened according to inclusion criteria (original research, adult patients, Western contexts, decision-making, palliative treatment or setting), yielding 37 articles for analysis. A narrative synthesis was created using the methods of thematic analysis, conceptual mapping, and critical reflection on the synthesis process. Results demonstrate that while a majority of patients want to participate in treatment decisions to some extent, most do not achieve their preferred levels of involvement because decisions are delayed and alternative treatment options are seldom discussed. The literature regarding the process of SDM itself remains scarce in palliative care. Further research is needed in order to better understand the longitudinal, interactive, and interdisciplinary process of decision-making in palliative care.
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 | 0.051 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".