How available are specialist medical doctors in Montenegro?
Bibliographic record
Abstract
Introduction: Health workers, especially highly skilled and trained staff, with their knowledge, experience, and skills, should enable the implementation of health policy, with the aim of preserving and improving the health of the population. The aim of this study was to determine the availability of medical doctors and specialist medical doctors in Montenegro, by type of specialization, i.e., to determine their age and gender structure, as compared to other countries in the European Region (ER). Materials and methods: Medical doctors (specialists, residents, and medical doctors without specialist training) represented the unit of observation in this study. In addition to the application of descriptive methods (absolute and relative numbers), indices of change in the number of doctors for the period between 2009 and 2021, indices of change in the age structure of doctors for the period between 2008 and 2021, indices of change in the prevalence of specialist doctors aged above 55 years (55+), by specialty, were calculated, as well as the density of different specialists per 100,000 population. Data related to the public sector, collected and published by the Institute of Public Health of Montenegro, were used. Results: The total number of doctors (specialists, residents, and medical doctors without specialist training) in Montenegro, in 2021, in the public sector, was by a third higher than in 2009, while the number of specialists increased by a quarter. The increase in the number of specialist medical personnel was significantly higher in hospitals, as compared to outpatient facilities (increase in the number of specialist doctors by 46% and residents by 41%, compared to 23% and 5% in outpatient facilities). In 2021, the highest percentage of specialist staff was in the age group of 55 years and above (about 34%). Conclusion: The aforementioned statistical series of data, related to specialist doctors, residents, and medical doctors without specialist training, in outpatient and inpatient health care, indicate a significant shortage of these doctors in Montenegro, as compared to most countries in the European Region, as well as an inadequate age structure, especially for certain specialties where careful human resources planning should be applied. Better and more efficient management of human resources should provide for young doctors to begin with specialist training sooner.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".