Experiences of Obstetricians and Gynecologists in Teleconsultation with Medical Residents: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: To explore the experiences of specialists, residents, and experienced personnel of obstetrics and gynecology regarding telephone consultation by specialized residents and on-call expertsDESIGN: Qualitative study based on inductive content analysis.SETTING: Three departments of obstetrics and gynecology, affiliated to Mashhad University of Medical Sciences, Mashhad, Iran.POPULATION: A purposive sample of 16 specialists, residents and experienced staff.METHODS: Eighteen semi-structured interviews were conducted.RESULTS: Analysis of interview data resulted in 363 primary codes and six main themes including: “attempt to direct the process of telephone consultation”, “decision-making challenges of diagnostic-therapeutic plans for patients”, “attempt to verify the acquired findings”, “inefficacy in the face of life-threatening conditions”, “discriminations in legal confrontation with medical errors”, and “impact on emotions and personal life”.CONCLUSIONS: Process of teleconsultation between physician and resident is associated with numerous challenges. Formal training sessions and considering new approaches of teleconsultation and telemedicine are needed to be implemented in order to reinforce the reliability of patient information transfer.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".