Measuring experiences of facility-based care for pregnant women and newborns: a scoping review
Bibliographic record
Abstract
BACKGROUND: Access to high-quality, person-centred care during pregnancy and childbirth is a global priority. Positive experience of care is key in particular, because it is both a fundamental right and can influence health outcomes and future healthcare utilisation. Despite its importance for accountability and action, systematic guidance on measuring experience of care is limited. METHODS: We conducted a scoping review of published literature to identify measures/instruments for experience of facility-based pregnancy and childbirth (abortion, antenatal, intrapartum, postnatal and newborn) care. We systematically searched five bibliographic databases from 1 January 2007 through 1 February 2019. Using a predefined evidence template, we extracted data on study design, data collection method, study population and care type as reported in primary quantitative articles. We report results narratively. RESULTS: We retrieved 16 528 unique citations, including 171 eligible articles representing, 157 unique instruments and 144 unique parent instruments across 56 countries. Half of the articles (90/171) did not use a validated instrument. While 82% (n=141) of articles reported on labour and childbirth care, only one reported on early pregnancy/abortion care. The most commonly reported sub-domains of user experience were communication (84%, 132/157) and respect and dignity (71%, 111/157). The primary purpose of most papers was measurement (70%, 119/171), largely through cross-sectional surveys. CONCLUSION: There are alarming gaps in measurement of user experience for abortion, antenatal, postnatal and newborn care, including lack of validated instruments to measure the effects of interventions and policies on user experience. PROTOCOL REGISTRATION DETAILS: This review was registered and published on PROSPERO (CRD42017070867). PROSPERO is an international database of prospectively registered systematic reviews in health and social care.
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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 | 0.030 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".