Pioneering family physicians and the mechanisms for strengthening primary health care in India—A qualitative descriptive study
Bibliographic record
Abstract
India has one of the most unequal healthcare systems globally, lagging behind its economic development. Improved primary care and primary health care play an integral role in overcoming health disparities. Family medicine is a subset of primary care-delivered by family physicians, characterized by comprehensive, continuous, coordinated, collaborative, personal, family and community-oriented services-and may be able to fill these gaps. This research aims to understand the potential mechanisms by which family physicians can strengthen primary health care. In this qualitative descriptive study, we interviewed twenty family physicians, identified by purposeful and snowball sampling, who are among the first family physicians in India who received accredited certification in FM and were identified as pioneers of family medicine. We used the Contribution of Family Medicine to Strengthening Primary Health Care Framework to understand the potential mechanisms by which family medicine strengthens primary health care. Iterative inductive techniques were used for analysis. This research identifies multiple ways family physicians can strengthen primary health care in India. They are skilled primary care providers and support mid and low-level health care providers' ongoing training and capacity building. They develop relationships with specialists, ensure appropriate referral systems are in place, and, when necessary, work with governments and organizations to access the essential resources needed to deliver care. They motivate the workforce and change how care is delivered by ensuring providers' skills match the needs of communities and engage communities as partners in healthcare delivery. These findings highlight multiple mechanisms by which family physicians strengthen primary health care. Investments in postgraduate training in family medicine and integrating family physicians into the primary care sector, particularly the public sector, could address health disparities.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".