Factors influencing medical specialists’ dual practice in the Islamic Republic of Iran
Bibliographic record
Abstract
BACKGROUND: Dual practice (DP) is performing several different jobs at the same time and has effects on healthcare services delivery. AIMS: To identify the causes of medical specialists' tendency towards DP in the Islamic Republic of Iran. METHODS: We used a qualitative approach to identify the factors affecting DP in medical specialists in 2016. We used a purposive and outlier sampling method to conduct semistructured deep interviews with 14 key informants. The data analysis was performed simultaneously with data collection using thematic content analysis by MAXQDA (version 10.0). Interviews continued up to data saturation. The quality of the study was ensured by addressing the criteria of Guba and Lincoln. RESULTS: The results of the interviews showed six themes and 16 subthemes for specialists' propensity to DP. Major themes included financial incentives, cultural attitudes about professional identity of physicians, experience and academic level of specialists, controlling approaches in the public sector, available infrastructure for responding to the population needs in the public sector, and regional characteristics of health service locations. CONCLUSIONS: Medical specialists' DP is a multidimensional issue, influenced by different factors such as financial incentives, cultural attitudes and available infrastructure. Considering the capacities and conditions of each country, control and management of this phenomenon require regulatory and incentive mechanisms, which in the long term can modify private and public sector differences and increase the willingness of doctors to work in the public sector.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".