Understanding and measuring clinician-patient relationship qualities in the setting of serious illness to promote healthcare quality and equity: A manuscript-based thesis
Bibliographic record
Abstract
Background: Serious illnesses pose many challenges to patients and their caregivers.They contribute to multiple forms of distress: physical, psychological, social, and spiritual.Relationships, including those between clinicians and patients, have the potential to mitigate patient suffering and promote experiences of growth and integrity through the illness journey and up to and beyond the end of life.Indeed, they are critical to processes of meaning making and the maintenance of these relationships is often the focus of patients' goals.Threats to the quality of clinical relationships are many and include systemic factors, such as poor healthcare access or clinician time, and interpersonal factors, such as language concordance and implicit bias.Despite their importance, we know little about the experience of clinician-patient relationships in the setting of potential interpersonal challenges, and little about how to measure relationship quality as a primary outcome of research and care.Objectives: (1) To examine the scope of current literature on valid survey instruments that assess the quality of relationships between people affected by serious illness and clinicians; and (2) To explore the qualities of healing clinician-patient relationships from the perspective of patients from visible minority groups who have received palliative care in Canada. Methods:In the first manuscript, we conducted a scoping review based on Arksey and O'Malley's (2005) framework and PRISMA-ScR guidelines.We included studies from CINAHL, Embase, and PubMed that describe the validation of survey instruments assessing care experiences of patient populations affected by serious illness.We categorized relationshipfocused items based on empirically derived domains of relationship quality.In the second Measuring the quality of patient-provider relationships in serious illness
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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 | 0.017 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".