Understanding the complex relationships among actors involved in the implementation of public-private mix (PPM) for TB control in India, using social theory
Bibliographic record
Abstract
BACKGROUND: Public Private Partnerships (PPP) are increasingly utilized as a public health strategy for strengthening health systems and have become a core component for the delivery of TB control services in India, as promoted through national policy. However, partnerships are complex systems that rely on relationships between a myriad of different actors with divergent agendas and backgrounds. Relationship is a crucial element of governance, and relationship building an important aspect of partnerships. To understand PPPs a multi-disciplinary perspective that draws on insights from social theory is needed. This paper demonstrates how social theory can aid the understanding of the complex relationships of actors involved in implementation of Public-Private Mix (PPM)-TB policy in India. METHODS: Ethnographic research was conducted within a district in a Southern state of India over a 14 month period, combining participant observations, informal interactions and in-depth interviews with a wide range of respondents across public, private and non-government organisation (NGO) sectors. RESULTS: Drawing on the theoretical insights from Bourdieu's "theory of practice" this study explores the relationships between the different actors. The study found that programme managers, frontline TB workers, NGOs, and private practitioners all had a crucial role to play in TB partnerships. They were widely regarded as valued contributors with distinct social skills and capabilities within their organizations and professions. However, their potential contributions towards programme implementation tended to be unrecognized both at the top and bottom of the policy implementation chain. These actors constantly struggled for recognition and used different mechanisms to position themselves alongside other actors within the programme that further complicated the relationships between different actors. CONCLUSION: This paper demonstrates that applying social theory can enable a better understanding of the complex relationship across public, private and NGO sectors. A closer understanding of these processes is a prerequisite for bridging the gap between field-level practices and central policy intentions, facilitating a move towards more effective partnership strategies for strengthening local health systems. The study contributes to our understanding of implementation of PPP for TB control and builds knowledge to help policy makers and programme managers strengthen and effectively implement strategies to enable stronger governance of these partnerships.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| 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".