Measuring the quality of patient-provider relationships in serious illness: A scoping review
Bibliographic record
Abstract
Background: People affected by serious illness face several threats to their well-being: physical symptoms, psychological distress, disrupted social relations, and spiritual/existential crises. Relationships with clinicians provide a form of structured support that promotes shared decision-making and adaptive stress coping. Measuring relationship quality may improve quality assessment and patient care outcomes. However, researchers and those promoting quality improvement lack clear guidance on measuring this. Aim: To identify and assess items from valid measures of patient-provider relationship quality in serious illness settings for guiding quality assessment. Design: Scoping review. Data sources: We identified peer-reviewed, English-language articles published from 1990 to 2023 in CINAHL, Embase, and PubMed. Eligible articles described the validation of measures assessing healthcare experiences of patient populations characterized by serious illness. We used Clarke et al.’s theory of relationship quality to assess relationship-focused items. Results: From 3868 screened articles, we identified 101 publications describing 47 valid measures used in serious illness settings. Measures assessed patients and other caregivers. We determined that 597 of 2238 items (26.7%) related to relationships. Most measures ( n = 46) included items related to engaging the patient as a whole person. Measures evaluated how providers promote information exchange ( n = 35), foster therapeutic alliance ( n = 35), recognize and respond to emotion ( n = 27), and include patients in care-related decisions ( n = 23). Few instruments ( n = 9) assessed patient self-management and navigation. Conclusions: Measures include items that assess patient-provider relationship quality in serious illness settings. Researchers may consider these for evaluating and improving relationship quality, a patient-centered care and research outcome.
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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 | 0.028 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".