Changes in primary health care in response to the COVID-19 pandemic in Montenegro
Bibliographic record
Abstract
Introduction: Primary health care is considered the basis, not only for meeting healthcare needs, but also for responding to crisis situations, such as the crisis caused by the outbreak of the COVID-19 pandemic. Aim: The aim of this study is to describe changes in primary health care caused by the outbreak of COVID-19 in Montenegro, with a focus on the organization and provision of health services. Methods: This is a descriptive observational study with the following main outcome measures: number and manner of visits and services, including e-services, prescribed antibiotics and prescriptions according to the diagnosis, per 1,000 inhabitants, in the year 2020, as compared to the same indicators in 2019. Data were generated from the health statistics information system of the Institute of Public Health of Montenegro. Demographic data from official statistics were used. Results: The global pandemic has changed the functioning of primary health care, and ad hoc solutions have been found, in terms of infrastructure, the health workforce, and new e-health services. As compared to 2019, in 2020, the total number of visits per 1,000 inhabitants decreased by 12%, while home visits increased by 15%, in the third quarter. Online scheduling (e-order) was the most commonly used e-service (accessed 983,212 times in 2020). The prescribed antibiotics did not exceed the monthly rates from 2019 (per 1,000 inhabitants). The number of prescriptions issued (per 1,000 inhabitants), in 2020, increased by 8% for the diagnoses for which doctors prescribed the most prescriptions (per 1,000 inhabitants). Conclusion: The COVID-19 pandemic has changed the functioning of primary health care, with a transition from office visits to - home visits, telephone consultations, and e-services, without physical contact. However, there is room for improvement in meeting health needs in the area of prevention and treatment of other diseases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".