Patient-Centered Surgical Care for Children in Low and Lower-Middle Income Countries (LMICs) - A Systematic Scoping Review of the Literature
Bibliographic record
Abstract
PURPOSE: Studies exploring patient-centered care (PCC) in pediatric surgery have been disproportionately concentrated in high-income countries. This review aims to characterize the adoption of key PCC domains in low and lower-middle income countries (LMICs). METHODS: Seven databases were searched from inception until January 2023 to retrieve relevant articles in pediatric surgery in LMICs. We focused on six key PCC domains: patient-reported outcomes (PROs), patient-reported experiences (PREs), shared decision-making (SDM), patient/parent education, patient/parent satisfaction, and informed consent. RESULTS: Of 8050 studies screened, 230 underwent full-text review, and 48 were finally included. Most were single-center (87.5%), cross-sectional studies (41.7%) from the South-East Asian (35.4%) and Eastern Mediterranean regions (33.3%). Studies most frequently focused on postoperative care (45.8%) in pediatric general surgery (18.8%), and included 1-3 PCC domains. PREs (n = 30), PROs (n = 16) and patient/parent satisfaction (n = 16) were most common. Informed consent (n = 2) and SDM (n = 1) were least studied. Only 13 studies directly elicited children's perspectives. Despite all studies originating in LMICs, 25% of first and 17.8% of senior authors lacked LMIC affiliations. CONCLUSION: The adoption of PCC in LMICs appears limited, focusing predominantly on PROs and PREs. Other domains such as informed consent and SDM are rarely addressed, and the voice of children and young people is rarely heard in their care. Opportunities to enhance PCC in LMICs abound, with the potential to improve the surgical care of children in resource-limited settings. LEVEL OF EVIDENCE: III.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".