Workshop: Quality and equity in primary care in European countries, Canada, Australia and New Zealand.
Bibliographic record
Abstract
A major challenge in health services research is to show what configurations of primary health care are associated with better outcomes, in terms of quality and equity. Today, such evidence is still incomplete. Through the QUALICOPC (Quality and Costs in Primary Care in Europe) study new knowledge in this area has been acquired. Between 2011 and 2013 data was collected by means of surveys among around 7000 General Practitioners (GPs) and 70 000 patients in 32 European countries, Canada, New Zealand and Australia. An important asset of this study is that the data of the patients can be linked to the data of the GP they visited. For the survey 4 types of questionnaires were used: a GP questionnaire, a questionnaire about Patients’ Experiences, a Patients’ Values questionnaire (about what they find important) and one about the GP practice characteristics. The presentations held during this workshop will focus on new insights gained through this study related to quality (measured through patients’ experiences and values and avoidable hospitalisation) and equity (in terms of access, treatment and outcomes). The presentation focuses on variation between countries on these aspects and touches upon potential predictors for these variances such as patients’ propensity to seek care and GPs’ behaviour. Key messages: This workshop provides insight in the variation related quality and equity of primary care in 35 countries. Important predictors for variation, such as patients’ propensity to seek care for avoidable hospitalization, will be discussed.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".