A taxonomy of patient experiences of access and continuity in interprofessional primary care teams
Bibliographic record
Abstract
Context: Patient experiences of access to care and continuity are essential to support health service improvements centred on patients’ needs. The ability to reach and engage in the care process varies considerably depending on patients’ socio-demographic characteristics. There is a need to understand these experiences to address inequitable access issues. Objective: To identify different profiles of access and continuity experiences of patients registered with interprofessional primary care teams. Study Design and Analysis: This cross-sectional study was conducted from September 2022 to April 2023. We used latent class analysis (LCA) to identify patients’ profiles based on 18 variables (access, continuity, chronic conditions, health status perception, clinic size, area) and multinomial logistic regression to analyze the class membership association with eight socio-demographic characteristics. Setting: 104 clinics across 14 regions of Quebec, Canada. Population Studied: 121,570 registered patients over 18 years of age with an email address in the electronic medical record. Outcome Measures: The optimal number of profiles (five) was determined using LCA measures (AIC, BIC, entropy). Results: Two profiles of patients in urban areas experienced difficulties obtaining care due to lack of availability of their physician: 1) “Mental health needs” (37% of respondents) perceived their mental health as poor and 2) “Physical health needs” perceived their physical health as poor (7%). 3) “Booking elsewhere” (28%) experienced difficulties reaching the clinic and consulted another clinic. 4) “Challenging booking” (12%) experienced difficulties reaching and traveling to the clinic. 5) “Rural chronic conditions” (16%) experienced difficulties with all access and continuity features, had a chronic condition, were in a rural area, and consulted at the emergency room. Female gender and a low education level were associated with belonging to class 2; male gender, high education and financial status with class 3; young age and low social support with class 4; elderly and low financial status with class 5; new immigrants with classes 1 and 5. Conclusions: These results highlight specific access barriers and potential areas for improvement based on patient experiences, such as difficulties related to physician availability for patients with mental health needs, reaching the clinic, and continuity of care for patients with social vulnerabilities and chronic conditions.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".