Value in Primary Healthcare – Measuring What Matters?
Bibliographic record
Abstract
High-performing and equitable healthcare systems are influenced by the strength of primary healthcare (PHC), which means that there should be special attention on this sector because we are changing how we monitor and improve overall care. Comprehensive data are the foundation for actionable information and are urgently needed in PHC because of the heterogeneity in both the demographics and the healthcare needs of the populations served. An ideal information system would combine multiple data sources such as electronic medical records (EMRs), administrative data and patient-reported information, drawing on the strengths of each to develop a comprehensive view of PHC. The purpose of this commentary is to draw attention to data gaps and offer suggestions about where and how this information could be obtained. Linked patient experience, EMRs and administrative data could be used in a learning health system to support decisions at the practice level and the jurisdictional level, where resources (financial and human) can be deployed to improve the quality of care, particularly when care is needed across sectors. The information gained from the analysis of these data are of high value for clinician/practice quality improvement efforts and for regional and jurisdictional health system planning and resource allocation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".