One year of COVID-19 and its impact on private provider engagement for TB: A rapid assessment of intermediary NGOs in seven high TB burden countries
Bibliographic record
Abstract
The COVID-19 pandemic has impacted health systems and health programs across the world. For tuberculosis (TB), it is predicted to set back progress by at least twelve years. Public private mix (PPM)has made a vital contribution to reach End TB targets with a ten-fold rise in TB notifications from private providers between 2012 and 2019. This is due in large part to the efforts of intermediary agencies, which aggregate demand from private providers. The COVID-19 pandemic has put these gains at risk over the past year. In this rapid assessment, representatives of 15 intermediary agencies from seven countries that are considered the highest priority for PPM in TB care (the Big Seven) share their views on the impact of COVID-19 on their programs, the private providers operating under their PPM schemes, and their private TB clients. All intermediaries reported a drop in TB testing and notifications, and the closure of some private practices. While travel restrictions and the fear of contracting COVID-19 were the main contributing factors, there were also unanticipated expenses for private providers, which were transferred to patients via increased prices. Intermediaries also had their routine activities disrupted and had to shift tasks and budgets to meet the new needs. However, the intermediaries and their partners rapidly adapted, including an increased use of digital tools, patient-centric services, and ancillary support for private providers. Despite many setbacks, the COVID-19 pandemic has underlined the importance of effective private sector engagement. The robust approach to fight COVID-19 has shown the possibilities for ending TB with a similar approach, augmented by the digital revolution around treatment and diagnostics and the push to decentralize health services.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".