Risk of Bias in Randomized Controlled Trials: An Analysis of Parent-Targeted Postnatal Education Interventions from Low and Middle-Income Countries
Bibliographic record
Abstract
Randomized controlled trials (RCTs) are vulnerable to internal and external bias, particularly when examining complex health behavioural interventions. The effects of postnatal education interventions on parent’s knowledge of caring for their newborn in low-and middle-income countries (LMICs) is a growing area of study. Therefore, the aim of this review was to assess the risk of bias (RoB) in such studies. MedLine, CINAHL, and SCOPUS were searched from January 2000 - October 2017 using key words such as RCT, parent-targeted, postnatal, education, interventions, and LMICs. Two reviewers screened title and abstracts and full text of eligible studies. Outcomes of interest were RoB measured using the Cochrane RoB tool, as well as intervention fidelity and contamination bias. Data were descriptively analyzed with 29 RCTs included. Highest risk of bias was in participant (55%) and personnel (76%) blinding with the lowest risk of bias in random sequence generation (76%), and attrition bias (72%). Overall, 89.7% of studies on postnatal parent-targeted education interventions in LMICs had a high RoB score in at least one domain. While difficult to avoid such biases, opportunities can be sought to minimize these during the design and conduct of future studies in this area.
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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 | 0.491 | 0.780 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.027 | 0.040 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".