The Emergence of Family Medicine and Its Impact on Primary Health Care: The Experience of India
Bibliographic record
Abstract
Many low- and middle-income countries are introducing family medicine (FM) to strengthen primary care and primary health care (PHC). However, there is little research on how FM emerges in a new context and how FM can strengthen PHC. Insight into these areas can help ensure implementation is effective and achieves the desired outcomes: improved health and well-being. In my dissertation, I use multiple methods to explore these two objectives. First, I look at how FM emerges in multiple countries and the trajectory of implementation using comparative policy analysis. Findings suggest four essential components, that often take place over several decades, including government commitment, educational reforms, the development of professional organizations and stakeholder buy-in. Second, I explore the implementation of FM in India and the role of early cohort family physicians in this process using a qualitative descriptive study. Findings show they developed and implemented the first FM training programs and professional organizations that supported the field's spread. They played key roles as leaders, educators, mentors, and advocates. Third, I delve into the potential mechanisms by which FM strengthens primary care and PHC through the experiences of early cohort family physicians in India using a qualitative descriptive study. They are skilled primary care providers that support the ongoing training of family physicians and mid and low-level healthcare providers and motivate the workforce. They change how care is delivered by ensuring providers' skills match the needs and engage communities as partners in healthcare. They develop relationships with specialists ensuring appropriate referral systems and, when necessary, work with governments safeguarding access to the necessary resources. Finally, using a cross-sectional survey, I assess the current landscape of FM in India to understand the implementation of FM to date and the impact of postgraduate training on family physicians and their ability to practise. Findings suggest that FM training is associated with increased confidence and skills and consequently family physicians deliver a broad range of services. In our sample, almost half of family physicians work in the primary care sector, and a greater proportion of family physicians work in rural areas compared to physicians overall.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| 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".