Homeless Patients Associate Clinician Bias With Suboptimal Care for Mental Illness, Addictions, and Chronic Pain
Bibliographic record
Abstract
Objective: To determine how accessible health care services are for people who are experiencing homelessness and to understand from their perspectives what impact clinician bias has on the treatment they receive. Methods: Narrative interviews were conducted with 53 homeless/vulnerably housed individuals in Ontario, Canada. Visit history records were subsequently reviewed at 2 local hospitals, for 52 of the interview participants. Results: Of the 53 participants only 28% had a primary care provider in town, an additional 40% had a provider in another town, and 32% had no access to a primary care provider at all. A subset of the individuals were frequent emergency department users, with 15% accounting for 75% of the identified hospital visits, primarily seeking treatment for mental illness, pain, and addictions. When seeking primary care for these 3 issues participants felt medication was overprescribed. Conversely, in emergency care settings participants felt prejudged by clinicians as being drug-seekers. Participants believed they received poor quality care or were denied care for mental illness, chronic pain, and addictions when clinicians were aware of their housing status. Conclusion: Mental illness, chronic pain, and addictions issues were believed by participants to be poorly treated due to clinician bias at the primary, emergency, and acute care levels. Increased access to primary care in the community could better serve this marginalized population and decrease emergency department visits but must be implemented in a way that respects the rights and dignity of this patient population.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".