The ResQu Index: A new instrument to appraise the quality of research on birth place
Bibliographic record
Abstract
OBJECTIVE: Place of birth is a known determinant of health care outcomes, interventions and costs. Many studies have examined the maternal and perinatal outcomes when women plan to give birth in hospitals compared with births in birth centres or at home. However, these studies vary substantially in rigour; assessing their quality is challenging. Existing research appraisal tools do not always capture important elements of study design that are critical when comparing outcomes by planned place of birth. To address this deficiency, we aimed to develop a reliable instrument to rate the quality of primary research on maternal and newborn outcomes by place of birth. STUDY DESIGN: The instrument development process involved five phases: 1) generation of items and a weighted scoring system; 2) content validation via a quantitative survey and a modified Delphi process with an international, multi-disciplinary panel of experts; 3) inter-rater consistency; 4) alignment with established research appraisal tools; and 5) pilot-testing of instrument usability. RESULTS: A Birth Place Research Quality Index (ResQu Index) was developed comprising 27 scored items that are summed to generate a weighted composite score out of 100 for studies comparing planned place of birth. Scale content validation indices were .89 for clarity, .94 for relevance and .90 for importance. The Index demonstrated substantial inter-rater consistency; pilot-testing confirmed feasibility and user-friendliness. CONCLUSION: The ResQu Index is a reliable instrument to evaluate the quality of design, methods and interpretation of reported outcomes from research about place of birth. Higher-scoring studies have greater potential to inform evidence-based selection of birth place by clinicians, policy makers, and women and their families. The Index can also guide the design of future research on place of birth.
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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 | 0.110 | 0.287 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.026 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".