Bibliographic record
Abstract
BACKGROUND: Few studies have examined the influence of physician workplace conditions on health care disparities. We compared 96 primary care clinics in New York, New York, and in the upper Midwest serving various proportions of minority patients to determine differences in workplace organizational characteristics. METHODS: Cross-sectional data are from surveys of 96 clinic managers, 388 primary care physicians, and 1701 of their adult patients with hypertension, diabetes mellitus, or congestive heart failure participating in the Minimizing Error, Maximizing Outcome (MEMO) study. Data from 27 clinics with at least 30% minority patients were contrasted with data from 69 clinics with less than 30% minority patients. RESULTS: Compared with clinics serving less than 30% minority patients, clinics serving at least 30% minority patients have less access to medical supplies (2.7 vs 3.4, P < .001), referral specialists (3.0 vs 3.5, P < .005) on a scale of 1 (none) to 4 (great), and examination rooms per physician (2.2 vs 2.7, P =.002) . Their patients are more frequently depressed (22.8% vs 12.1%), are more often covered by Medicaid (30.2% vs 11.4%), and report lower health literacy (3.7 vs 4.4) on a scale of 1 (low) to 5 (high) (P < .001 for all). Physicians from clinics serving higher proportions of minority populations perceive their patients as frequently speaking little or no English (27.1% vs 3.4%, P =.004), having more chronic pain (24.1% vs 12.9%, P < .001) and substance abuse problems (15.1% vs 10.1%, P =.005), and being more medically complex (53.1% vs 39.9%) and psychosocially complex (44.9% vs 28.2%) (P < .001 for both). In regression analyses, clinics with at least 30% minority patients are more likely to have chaotic work environments (odds ratio, 4.0; P =.003) and to have fewer physicians reporting high work control (0.2; P =.003) or high job satisfaction (0.4; P =.01). CONCLUSION: Clinics serving higher proportions of minority patients have more challenging workplace and organizational characteristics.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.067 | 0.006 |
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".