‘Maybe you should have a bowl of ice cream’: Inequities in patient-clinician interactions among individuals with chronic low back pain
Bibliographic record
Abstract
Prior literature has shown inequities in patient-clinician interactions experienced by individuals with chronic low back pain (CLBP) with underlying pain-related stigmatization and invalidation. Yet, there is a notable gap in understanding how these inequities intersect with multiple systems of oppression, including racism and sexism. This qualitative study examined intersectional perspectives and experiences of patient-clinician interactions among individuals with CLBP. Semi-structured interviews were conducted after the participants engaged in simulated enhanced or limited patient-clinician interactions as part of an experimental study. Participants were asked to compare the simulated patient-clinician interaction to their real-life patient-clinician interactions for their CLBP. The study included 50 participants with CLBP for at least three months and half the days in the past six months. Participants were Black and multi-racial women (n=14), Black and multi-racial men (n=12), non-Hispanic White women (n=12), and non-Hispanic White men (n=12). A basic qualitative approach with principles from constructivist grounded theory and intercategorical intersectional research were used to propose three core categories when describing inequities in patient-clinician interactions: higher-level systems (subcategories: institutional, community, macro-level), the patient-clinician interaction (subcategories: being taken seriously, person-centered care), and effects of the patient-clinician interaction (subcategories: indirect, direct effects). Inequities were identified across all categories, disproportionately affecting Black and multi-racial women. Black and multi-racial women also distinctly shared a wider range of both positive and negative patient-clinician interactions and effects from these interactions, and potential pathways to more equitable care. These findings highlight the need for multi-level interventions to promote more equitable care for individuals with CLBP. PERSPECTIVE: This qualitative study examined intersectional perspectives and experiences of patient-clinician interactions among individuals with CLBP. Multiple intersecting systems shaped inequities in patient-clinician interactions. Black and multi-racial women shared the broadest range of patient-clinician interactions, distinctly discussed intersecting systems of oppression, and highlighted pathways to more equitable care.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".