Socioeconomic status and allied health use: Among patients in an academic family health team.
Bibliographic record
Abstract
OBJECTIVE: To identify whether socioeconomic status is associated with allied health use among patients in a large academic family health team (FHT). DESIGN: Data were collected through a retrospective chart review using an electronic medical record system. SETTING: A large academic FHT in Ottawa, Ont. PARTICIPANTS: Patients with at least 1 in-person clinician encounter between January 1, 2012, and December 31, 2013. MAIN OUTCOME MEASURES: Descriptive statistics were used to compare patients who accessed allied health services with those who did not. We conducted logistic regression analyses to determine whether income quintile was independently associated with allied health use after adjusting for other patient characteristics. RESULTS: The inclusion criteria identified 2938 unique patients, of whom 949 (32.3%) saw an allied health provider(AHP) during the study period. While patients in the fourth income quintile had the greatest AHP use per person (41.2% of patients had at least 1 AHP visit), those in the lowest income quintile had the greatest mean number of AHPs seen(mean [SD] = 1.48 [0.80]). After adjustment, the odds of seeing an AHP were significantly increased with older age (odds ratio [OR] = 1.02, 95% CI 1.01 to 1.02) and female sex (OR = 1.81, 95% CI 1.48 to 2.22). Compared with patients in the highest income quintile, patients in the lowest (OR = 1.33, 95% CI 1.02 to 1.72) and fourth (OR = 1.88, 95% CI 1.33 to 2.66) income quintiles had significantly higher odds of seeing AHPs. CONCLUSION: Within an academic FHT, lower-income patients were more likely to use allied health services, suggesting equitable allocation of resources. We encourage other FHTs to similarly assess their allied health resource allocation as an important outcome for investments in Ontario FHTs.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".