Understanding disparities in primary care patient experience
Bibliographic record
Abstract
OBJECTIVE: To understand disparities in primary care patient experience. DESIGN: A serial cross-sectional study was conducted to understand disparities in patient experience at 2 time points (2014 and 2016). Disparities related to age, gender, neighbourhood income, and self-rated health were explored using 3 analytic approaches: stratification, logistic regression, and relative comparison across multiple demographic variables. SETTING: A multisite family health team in Toronto, Ont. PARTICIPANTS: Patients in the family medicine practice who completed e-mail surveys in 2014 (n = 1171, 19% response rate) and 2016 (n = 1823, 15% response rate). MAIN OUTCOME MEASURES: Patient-reported access (timely access when sick, access after hours) and patient-centredness (opportunity to ask questions, involvement in care decisions, enough time with provider). RESULTS: Performance for all measures improved between 2014 and 2016, with the greatest absolute improvement seen in access after hours (61% in 2014; 75% in 2016). Patients residing in low-income neighbourhoods reported worse patient experiences than those in high-income neighbourhoods did, as did patients with poor versus excellent self-rated health, even after adjustment for other variables. For example, in 2016, 60% of patients residing in low-income neighbourhoods reported timely access when sick versus 70% in high-income neighbourhoods (adjusted odds ratio of 0.67, 95% CI 0.47 to 0.95); 60% of patients with poor or fair self-rated health reported timely access when sick versus 72% with excellent self-rated health (adjusted odds ratio of 0.54, 95% CI 0.35 to 0.84). Comparing across demographic groups, patients with excellent self-rated health and poor or fair self-rated health reported the best and worst experiences, respectively, for all measures. In 2016, the average disparity between subgroups was largest for access after hours. CONCLUSION: In this setting, patient experience was worse for patients in lower-income neighbourhoods and those with poor or fair self-rated health. Access after hours demonstrated the greatest overall absolute improvement but also the greatest widening of disparities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".