OECD Patient-Reported Indicators Survey (PaRIS) in Canada: Results From the National Field Study
Bibliographic record
Abstract
Context: The Organization for Economic Co-operation and Development provides international standards for reporting on health system performance. While collecting and reporting patients’ experiences and outcomes is increasingly integrated into acute care, most healthcare services are provided in primary care. It is a significant gap that patient-reported measurement still needs to be improved in primary care. Objective: Compare, in Canada, nationally equivalent health outcomes and experiences of patients with chronic conditions treated in primary care to identify areas of excellence and improvement. Study Design and Analysis PaRIS-Survey in Canada is a two-phase cross-sectional study (pilot and main study). Setting or Dataset: Primary care practices across Canada. Population Studied: Inclusion criteria for providers are: a) practicing providers (e.g., family physicians, nurse practitioners) who have a patient panel. Inclusion criteria for patients are: a) aged 45 years or older; and b) having at least one registered contact with a recruited provider during the six months preceding the selection procedure. Intervention/Instrument: The patient and provider questionnaires were developed based on the framework of the PaRIS-OECD survey and approved by the Working party-PaRIS. Outcome Measures: The provider questionnaire asks about practice characteristics (34 items). The Main Patient Survey consists of 121 items and is organized around four domains: health status, symptoms, managing health, experiences of primary health care services, experiences of other health care services, and sociodemographic characteristics. Results: Phase 1. The pilot study included six provinces. 816 patients and 23 PCPs from 19 practices in 3 provinces (SK, ON, NB) participated. Patient response rates were 10% (SK), 28% (ON), and 47% (NB). No data were collected in BC, QC, and PEI. Several issues were raised during the pilot for participants’ recruitment, such as insufficient resources, and accessibility to the web-based survey. Phase 2. Data will be collected during the summer and fall of 2023. The results of data collection will be presented. Conclusions: Results from this unique Canadian multiphase study will provide a new generation of standardized patient-reported indicators used across 20 countries. These results will enable countries to learn from the approaches of others to improve the performance of primary care services for people living with chronic conditions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".