The landscape of family medicine in India – A cross-sectional survey study
Bibliographic record
Abstract
Family medicine was recognized as a distinct specialty in India in the early 1980s, but it is at an early stage of implementation. There are few training programs, and little is known about family physicians' training, perceptions, and current practices. This paper describes the findings from the first national survey of family medicine in India. We administered the Landscape of Family Medicine in India survey to members of the Academy of Family Physicians of India and used a respondent-driven sampling approach to increase our reach between November 2020 and March 2021. Descriptive statistics were used to describe the data. Chi-square tests of independence were used to explore differences between family physicians who completed full-time in-person training versus those who completed part-time, blended or distance training and to look for associations between services provided and the rurality of practice location. We had 272 respondents. 61.0% of respondents completed a full-time in-person residency program, while 39.0% completed a part-time distance or blended-type program. Most respondents reported that postgraduate training in family medicine increased their confidence in practice, their scope of primary care practice, and the ability to work as a team with non-physician primary care providers, irrespective of the type of training. Family physicians appear to engage in comprehensive practice, with 88.9% practicing outpatient family medicine. Our sample found that the proportion of family physicians working in rural areas is higher than the proportion of all physicians in India, with 39.3% of our sample working rurally. Those who work rurally were more likely to offer minor office-based surgeries, casting and splints, and conduct vaginal deliveries. 48.3% of respondents work principally in the primary care sector. Postgraduate family medicine training should be scaled up to support improving gaps seen in primary care and primary health care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".