Shared Decision Making and Patient-Centered Care in Israel, Jordan, and the United States: Exploratory and Comparative Survey Study of Physician Perceptions (Preprint)
Bibliographic record
Abstract
BACKGROUND Shared decision making (SDM) is a health communication model that evolved in Europe and North America and largely reflects the values and medical practices dominant in these areas. OBJECTIVE This study aims to understand the beliefs, perceptions, and practices related to SDM and patient-centered care (PCC) of physicians in Israel, Jordan, and the United States. METHODS A hypothesis-generating comparative survey study was administered to physicians from Israel, Jordan, and the United States. RESULTS A total of 36 surveys were collected via snowball sampling (Jordan: n=15; United States: n=12; Israel: n=9). SDM was perceived as a way to inform patients and allow them to participate in their care. Barriers to implementing SDM varied based on place of origin; physicians in the United States mentioned limited time, physicians in Jordan reported that a lack of patient education limits SDM practices, and physicians in Israel reported lack of communication training. Most US physicians defined PCC as a practice for prioritizing patient preferences, whereas both Jordanian and Israeli physicians defined PCC as a holistic approach to care and to prioritizing patient needs. Barriers to implementing PCC, as seen by US physicians, were mostly centered on limited appointment time and insurance coverage. In Jordan and Israel, staff shortage and a lack of resources in the system were seen as major barriers to PCC implementation. CONCLUSIONS The study adds to the limited, yet important, literature on SDM and PCC in areas of the world outside the United States, Canada, Australia, and Western Europe. The study suggests that perceptions of PCC might widely differ among these regions, whereas concepts of SDM might be shared. Future work should clarify these differences.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".