A survey of barriers and facilitators to caffeine use for apnoea of prematurity in low- and middle-income countries
Bibliographic record
Abstract
Despite its associated benefits which include better long-term pulmonary and neurodevelopmental outcome, the use of caffeine for apnoea of prematurity (AoP) has been limited in low- and middle-income countries (LMIC). To better understand current caffeine use, the barriers and facilitators to its use and perceptions and practices in LMIC which have a disproportionately high burden of prematurity. An anonymous online global survey was conducted, targeting healthcare providers working and training in paediatrics and/or neonatology in LMIC. A total of 181 respondents in 16 LMIC were included in the analysis; most were physicians working in publicly-funded urban tertiary hospitals. Most had received training in the use of caffeine for AoP (77%), reported expertise (70%) and confidence (96%) in its use, and had access to caffeine (65%). Caffeine availability was reported to be the greatest barrier (48%) and the greatest facilitator (37%). Other common barriers included cost (31%), access (7%) and policies or guidelines on caffeine use (7%); other common facilitators included policies or guidelines on caffeine use (11%), access (10%), staff/other providers’ acceptance of caffeine as an appropriate treatment (9%) and the availability of staff to administer caffeine (8%). Most (79%) noted that access to caffeine was important, 92% agreed that caffeine improves quality of care, and 95% agreed that caffeine improves patient outcome. Improving availability and access to low-cost caffeine will be key to increasing caffeine use in LMIC. AoP: Apnoea of Prematurity; LMIC: low- and middle-Income countries; REDCap: Research Electronic Data Capture.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".