Factors associated with patients' experience of access to their primary health care clinic: a multilevel analysis
Bibliographic record
Abstract
Context: Understanding patients’ experience accessing primary health care (PHC) is necessary in order to move toward better service organization and more equitable PHC access. Objective. This study aims to examine individual, organizational, and contextual factors associated with patients’ experience of access to their multidisciplinary primary healthcare clinic. Study Design and Analysis. This cross-sectional study builds on survey data collected in multidisciplinary PHC clinics. Between September 2022 and June 2023, online questionnaires were sent to patients attached to a family physician and to PHC professionals and administrative staff. Multi-level logistic regression models were fit. Analyses were stratified by the number of consultations in the last 12 months: between 1 and 5 or over 5 consultations. Settings. 104 PHC clinics across 14 regions of Quebec, Canada. Population studied. A total of 122,397 patients and 999 family physicians, 107 nurse practitioners and 411 administrative staff nested into 104 clinics answered the survey. Instrument. A patient-reported experience survey on primary care. A self-reported survey based on the advanced access model for professionals and administrative staff. Outcomes measures. Two patient-reported experiences were assessed: 1) difficulty having an appointment with regular family physician or nurse practitioners, and 2) unmet healthcare needs. Results: The results indicate that some organizational and contextual-level factors were associated with the difficulty in accessing regular provider and reporting unmet needs. Organizational factors including estimation of demand and supply, use of a referral algorithm, and strategies to optimize consultations were associated with a better experience of care. Patients from medium size clinics compared to small clinics had better experience of care for both outcomes. The stratified analysis indicated similar results related to outcomes for patients who consulted at the clinic 1-5 times in the last 12 months. Conclusions: According to patient-reported experiences, this study suggests that mechanisms fostering PHC access for attached patients should prioritize organizational processes over individual characteristics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".