A national roadmap for improving children surgical care: an experience from Tanzania
Bibliographic record
Abstract
PURPOSE: Nearly 1.75 billion children lack access to basic surgical care. The majority are in low- and middle-income countries (LMICs) where 50% of the population are children. Our aim was to develop a policy-level, action-orientated, and implementable strategy to improve children surgery in Tanzania. METHODS: A bottom-up participatory approach was used for needs assessment and priority setting. This started 2 years after the launch of Tanzania National Surgical, Obstetrics, and Anesthesia Plans (NSOAPs). Steps taken were: (i) stakeholder identification and engagement, (ii) desk review of existing research, (iii) focused research on access to children surgery, (iv) site visits and geographical mapping of the reach of selected hospitals, and (v) presentation to the ministry of health for validation. Findings were summarized in line with the NSOAP's building blocks. RESULTS: A bottom-up approach was feasible in identifying children surgical care challenges of policy priority. We noted that regional hospitals are the main provider of children surgery but majority lacked the necessary resources and were beyond recommended 2-hour reach. A super hub-hub-spoke model is a feasible model to pragmatically address patient, provider, facility and national challenges at all levels of healthcare system. CONCLUSION: Our findings propose a roadmap to practically achieve access to children surgery, complementing existing NSOAPs. It highlights feasibility of the approach in developing context relevant interventions that could guide integration of surgery in existing national plans. Developing a functional surgical system in LMICs should be pragmatic to improve overall quality of children surgical care despite limited resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".