Factors associated with patients' experience of access to their multidisciplinary primary health care clinic: A multilevel analysis
Bibliographic record
Abstract
BACKGROUND: Understanding patients' experiences accessing primary health care (PHC) is necessary to improve service organisation. This study aims to examine individual, organisational, and contextual factors associated with patients' experience of accessing the multidisciplinary PHC clinic to which they are attached. METHODS: This cross-sectional study builds on survey data collected in multidisciplinary PHC clinics located in 14 regions in the province of Quebec (Canada). Between September 2022 and June 2023, an online questionnaire was sent to patients with an email contact and attached to a family physician. Two patient-reported experience measures were assessed: (1) difficulty obtaining an appointment with their regular family physician or nurse practitioner and (2) perceived unmet healthcare needs. A self-reported online questionnaire based on the advanced access model was also sent to PHC professionals and administrative staff to assess the use of advanced access strategies in their practice. Multilevel logistic regression models were fit. Stratified analyses were conducted according to the number of consultations received. FINDINGS: In total, 122,397 patients and 847 family physicians, 97 nurse practitioners and 347 administrative staff nested into 104 clinics answered the survey. In the overall sample, having a chronic disorder was the only individual factor associated with the patient experience of access. Organizational factors including estimation of demand and supply, use of a referral algorithm, and strategies to optimise consultations were associated with a better access experience. Patients from medium size clinics compared to small clinics had better experiences of care for both outcomes. Stratified analysis indicated similar results for patients who consulted at the clinic 1-5 times in the last 12 months as observed in the overall sample. CONCLUSIONS: This study indicates that enhancing organizational processes can improve patients' access experiences.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".