Measuring the performance of urban healthcare services: results of an international experience
Bibliographic record
Abstract
The objective of this paper is to apply a framework for country-level performance assessment to the cities of Montreal, Canada, and Barcelona, Spain, and to use this framework to explore and understand the differences in their health systems. The UK National Health Service Performance Assessment Framework was chosen. Its indicators went through a process of selection, adaptation and prioritisation. Most of them were calculated for the period 2001-3, with data obtained from epidemiological, activity and economic registries. Montreal has a higher number of old people living alone and with limitations on performing one or more activities of daily life, as well as longer hospital stays for several conditions, especially in the case of elderly patients. This highlights a lack of mid-term, long-term and home care services. Diabetes-avoidable hospitalisation rates are also significant in Montreal, and are likely to improve following reforms in primary care. Efficient health policies such as generic drug prescription and major ambulatory surgery are lower in Barcelona. Rates of caesarean deliveries are higher in Barcelona, owing to demographics and clinical practice. Waiting times for knee arthroplasty are longer in Barcelona, which has triggered a plan to reduce them. In both cities, avoidable mortality and the prevalence of smoking have been identified as areas for improvement through preventive services. In conclusion, performance assessment fits perfectly in an urban context, as it has been shown to be a useful tool in designing and monitoring the accomplishment of programmes in both cities, to assess the performance of the services delivered, and for use in policy development.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".