Situational analysis of general practitioners using a forecasting approach until 2025 and a multi-state Markov model: A retrospective longitudinal study
Bibliographic record
Abstract
Introduction: Undesirable working conditions, insufficient professional development and other labour market pressures have significantly impacted the status of general practitioners (GPs). This study aimed to conduct a situational analysis of GPs in Iran using a forecasting approach until 2025. Methods: Data were collected concurrently through direct contact, data matching among databases and tracking among graduates from four clusters of medical science universities over the past decade. This retrospective longitudinal study determined the status of GPs over consecutive years. Multi-state Markov and binary logistic regression analyses were performed using R and Stata 14. Results: Of 430 graduates over the past decade, 94% were successfully identified. Only 20% of the graduates remained active as GPs. The greatest fluctuations in transfer occurred in the third year after graduation, with the remaining proportion of GPs dropping to less than 50%. The probability of remaining as GPs was 0.76 per year, while the highest transition was observed towards specialisation (0.12). Additionally, 2% of the GPs chose not to work, and less than 1% transitioned to a different specialty. Based on the transfer matrix for 2025, only 19% of the GPs were projected to remain, with the majority (59%) transitioning to specialisation. Conclusion: The transfer probability varies across different years, indicating higher flow rates among GPs. However, only a limited number of GPs are projected to remain until 2025. A comprehensive set of interventions should be considered, spanning the pre-medical stage, during education and after graduation, to mitigate the factors contributing to GPs leaving their profession.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".