Current employment characteristics and career intentions of Lithuanian dentists
Bibliographic record
Abstract
BACKGROUND: The present survey explored the current employment profile and future career intentions of Lithuanian general dentists and specialists. METHODS: A census sampling method was employed with data collected by means of a structured questionnaire that inquired about demographics, different employment-related aspects (practice type and location, working hours, perceived lack of patients, etc.), and future career intentions (intent to emigrate, to change profession, or the timing of retirement). The final response rate was 67.6% corresponding to 2,008 respondents. RESULTS: The majority of all dentists work full or part-time in the private dental sector, more than one third of them owns a private practice or rents a dental chair. A minority of dentists works in the public dental sector. According to the survey, 26.6% of general dentists and 39.2% of dental specialists works overtime (> 40 hours per week; P < 0.001) and practice in multiple clinics (1.4 ± 0.6 and 2.0 ± 1.2, respectively; P < 0.001). One third of general dentists (31.3%) and dental specialists (31.4%) stated to have a low number of patients (P > 0.05). The majority (68.9% of general dentists and 65.9% of dental specialists) plans to work after the retirement age (P > 0.05). Emigration as an option for their professional career is being considered by 10.8% of general dentists and 8.3% of dental specialists (P > 0.05). Working either full or part-time in private practices (OR = 4.3) and younger age (≤ 35 years; OR = 2.2) are the two strongest predictors for a perceived insufficient number of patients. CONCLUSIONS: One third of dentists in Lithuania work long hours and lack patients. Many dentists practice in multiple locations and plan to retire after the official retirement age. Some dentists and dental specialists plan to emigrate. The perceived shortcomings within the dental care system and workforce planning of dentists need to be addressed.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".