Policy and service delivery proposals to improve primary care services in low-income and middle-income country cities
Bibliographic record
Abstract
The landscape of primary care services in low-income and middle-income country cities is diverse and dynamic, yet the quality of care received is too often low and the financial cost to the patient high. In the second Paper in this Series, we argue that shaping the primary care market is likely to provide larger returns to scale than individual quality improvement initiatives. Among other things, the market can be shaped by regulation and targeted public investment to crowd out poor providers and motivate those that remain to improve. Additional supply-side initiatives for which there is evidence include measures to educate and motivate the workforce, skill substitution and formation of clinical primary care teams, information technology, and improving the supply of medicines and diagnostics. Demand-side measures include reducing out-of-pocket expenses and promoting health literacy and user advocacy. Research is urgently needed into access for people who are unregistered (eg, those who sleep on the streets), those in peri-urban areas and towns, and on cost-effectiveness, and sustainability of beneficial interventions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".