Financial characteristics and satisfaction of physicians practicing in Lebanon: a survey study.
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate the financial characteristics and level of satisfaction of physicians practicing in Lebanon. METHODS: We conducted an anonymous, interviewer-administered phone survey of physicians practicing medicine in Lebanon. We conducted both descriptive and regression analyses. RESULTS: Of 778 invited physicians, 546 participated in the survey (70% response rate). Their mean age was 47.4 and they were predominantly male (85.9%) and married (87.0%). Reported monthly income varied widely with 47.2% earning less than US$ 2,000, 46.3% earning between $ 2,000 and $ 6,000 and 6.3% earning more than $ 6,000. Only 14.2%, 4.1%, and 3.1% respectively reported having life insurance, disability insurance and a retirement plan. A quarter of participants reported being either somewhat unsatisfied (17.6%) or very unsatisfied (8.1%) with their medical career. A lower degree of satisfaction in professional career was independently associated with female physicians, graduation from a Western European medical school and a lower monthly income. As for the perception of own career's future, 36.7% thought there was no possibility of improvement. CONCLUSION: About half of physicians practicing in Lebanon report earning less than US$ 2,000 per month and about a quarter are not satisfied with their professional career.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".