Patterns of patient experience with primary care access in Australia, Canada, New Zealand and Switzerland: a comparative study
Bibliographic record
Abstract
OBJECTIVE: Access to primary care (PC) is vital, but complex to define and compare between settings. We aimed to generate a typology of patients' access patterns across countries using a novel inductive approach. DESIGN: Cross-sectional surveys. SETTING: Australia, Canada, New Zealand and Switzerland between 2012 and 2014 as part of the QUALICO-PC project. PARTICIPANTS: Data were collected from 1306 general practices and 10 000+ patients, with nine patients per practice. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Typology of access. RESULTS: Three axes were retained, explaining 23% of the total variance: (i) 'temporal and geographical access'; (ii) 'frequency of access and unmet healthcare needs'; and (iii) 'affordability and frequency of access'.Based on the three axes, we identified four clusters of patients: (i) patients reporting overall good access to PC; (ii) frequent users with unmet healthcare needs; (iii) under-users with financial barriers; and (iv) users with poor time/geographical access.Better access to PC was experienced in Switzerland and New Zealand, while worst access was reported in Canada, where most of the time and geographical barriers were reported. Most financial barriers were observed in Australia and New Zealand. Frequent users with some level of unmet healthcare needs are prevalent in all four countries. CONCLUSIONS: Four main groups of patients with different patterns of access were identified: (i) good access; (ii) geographical and time barriers; (iii) financial barriers; and (iv) frequent users with unmet healthcare needs. Differences in access between the four countries are substantial.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".