Do socially complex patients seek primary care from clinics specifically designed to meet their needs?
Bibliographic record
Abstract
ABSTRACTObjectivesTo determine the relationship between known social complexity and model of primary care service deliveryApproachThe impacts of the social determinants of health are well described. To understand the contribution of specific factors on primary care service use we linked social data in the Population Health Research Data Repository at the Manitoba Centre for Health Policy to health system data. We included all patients visiting a Winnipeg clinic at least three times between 2010 and 2013. We allocated each participant to the primary care provider providing the majority of their care; and each provider was assigned to the model of care where they provided the majority of their clinical care. We developed eleven new indicators to describe social complexity such as: children in care, low income quintile, income assistance (welfare), high residential mobility, and involvement with the justice system. Results The cohort included 626,264 unique individuals of whom 53.1% were female. The majority of participants received their care from the fee for service (FFS) model (511,763) followed by 76,261 assigned to “reformed FFS”. 16,536 and 12,178 were assigned to the 2 team-based care alternative funded models and 9,526 to the teaching clinic model. Patients with social complexities, except for newcomers, were more likely to attend the alternative funded clinics. The patients these clinics served were generally very complex with over 15% having more than 5 complexities compared to less than 5% of those attending the FFS models. Twice as many patients in the FFS models (60%) had no complexities compared to the alternative funded models.ConclusionThe availability of social data in population health repositories provides new opportunities to understand the distribution of these social factors amongst care providers and the impact of each on the health of populations. This new understanding can support focused interventions to address specific social risk factors and provide the evidence to support different models of primary care service delivery.
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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 | 0.009 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".