Patient poverty and workload in primary care: study of prescription drug benefit recipients in community health centres.
Bibliographic record
Abstract
OBJECTIVE: To determine if patient poverty is associated with increased workload for primary care providers (PCPs). DESIGN: Linkage of administrative data identifying patient poverty and comorbidity with survey data about the organizational structure of community health centres (CHCs). SETTING: Ontario's 73 CHCs. PARTICIPANTS: A total of 64 CHC sites (N=63 included in the analysis). MAIN OUTCOME MEASURES: Patient poverty was determined in 2 different ways: based on receipt of Ontario Drug Benefits (identifying recipients of welfare, provincial disability support, and low-income seniors' benefits) or residence in low-income neighbourhoods. Patient comorbidities were determined through administrative diagnostic data from the CHCs and the Institute for Clinical Evaluative Sciences. Primary care workload was determined by examining PCP panel size (the number of patients cared for by a full-time-equivalent PCP during a 2-year interval). RESULTS: The CHCs with higher proportions of poor patients had smaller panel sizes. The smaller panel sizes were entirely explained by the medical comorbidity profile of the poor patients. CONCLUSION: Poor patients generate a higher workload for PCPs in CHCs; however, this is principally because they are sicker than higher-income patients are. Further information is required about the spectrum of services used by poor patients in CHCs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".