Profiling patterns of patient experiences of access to care and continuity at team-based primary healthcare clinics
Bibliographic record
Abstract
Context: Access to primary healthcare services is a core lever for reducing health inequalities. The ability to reach and engage in the care process varies considerably depending on patients’ socio-demographic characteristics which we need to understand to address inequitable access issues. Objective: To identify different profiles of access to care and continuity experiences of patients registered at team-based primary healthcare clinics. Study Design/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 nine components of access and continuity experiences and multinomial logistic regression to analyze their association with ten characteristics related to patient sociodemographic and their clinic characteristics. Setting: 104 PHC clinics across Quebec, Canada. Setting/Dataset: 121,570 registered patients over 18 years of age with an email address available in their electronic medical record. Measures: The optimal number of profiles (four) was determined using LCA measures (best model determined by AIC, BIC, and entropy). Results: "Easy access and continuity" (42%) was characterized by ease in almost all access and continuity components. Three profiles were characterized by diverging access and/or continuity difficulties: "Challenging booking" (32%) represented patients having to try several times to obtain an appointment at their clinic; "Challenging continuity" (9%) those having to repeat information that should have been in their file; "Access and continuity barriers" (16%) characterized difficulties with all access and continuity components. Female gender and poor perceived health significantly increased the risk of belonging to the three difficulties profiles by 1.5. Being a recently arrived immigrant (p=0.036), having less than a high school education (p=0.002) and being registered at a large clinic (p<0.001) were associated with experiencing booking difficulties. Having at least one chronic condition (p=0.004) or poor perceived mental health (p=0.048) were associated with experiencing continuity difficulties. Conclusions: Our results showed potential areas for improvement, such as facilitating appointment booking for recently arrived immigrants and patients with low education, integrating interprofessional collaboration practices for patients with chronic conditions and improving care coordination for patients with mental health needs.
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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 | 0.002 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".