Primary Health Care Appointments and Hospital Stay: An Impact Analysis
Bibliographic record
Abstract
The study of avoidable hospitalizations has gained international prominence due to its potential to assess the performance of healthcare systems. In Canada and Spain, these hospitalizations are analyzed through Ambulatory Care Sensitive Conditions (ACSC), indicating situations that could have been pre-vented or treated without hospitalization. In Portugal, this concept is represented by the term ICSCSP, focusing on care provided in Primary Health Care (PHC). The data analysis in this study aims to determine the impact that medical appointments at PHC may have on the number of hospitalizations, namely, to determine whether where the number of medical appointments is greater, the number of hospital stays is lower. During the COVID-19 pandemic in Portugal, many hospitalizations of the elderly were due to the decompensation of chronic diseases, highlighting the importance of access to PHC during health emergencies. Data pre-processing was carried out using the Pandas library in Python, merging two datasets monitoring the evolution of hospitalizations and medical appointments in PHC. Despite some challenges encountered during the analysis, such as population bias in district comparisons and the need to adjust metrics to properly reflect the relationship between appointments and hospital stays, it was concluded that the number of appointments in PHC does not have a direct impact on hospitalizations. For a more accurate analysis, it would be necessary to consider other factors, such as patient and district characteristics, and conduct more targeted studies, especially after disruptive events like the COVID-19 pandemic. This more detailed analysis would allow for a better understanding of the relationship between medical appointments and hospitalizations, contributing to improvements in the healthcare system.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".