Implementation of the London Measure of Unplanned Pregnancy in routine antenatal care in London: a mixed-methods evaluation
Bibliographic record
Abstract
Objective: To evaluate the implementation of the London Measure of Unplanned Pregnancy (LMUP) in antenatal care. Design: Mixed methods evaluation of a pilot. Setting: Antenatal care at University College London Hospital and Homerton Hospital, England, 2019-2022. Population: Pregnant women attending antenatal care at one of the sites during the evaluation. Methods: Quantitative and psychometric analysis of anonymous data and qualitative analysis of interviews and focus groups with women and midwives, using a Framework Analysis. Main Outcome Measures: Acceptability of the inclusion of the LMUP, measured by completion rates and women’s and midwives opinions. Results: Completion of the LMUP at UCLH stabilised at around 70% and the LMUP performed as expected. Asking the LMUP at antenatal booking appointments is feasible and acceptable to women and midwives. Advantages of asking the LMUP, highlighted by participants, include providing additional support and personalising care. Midwives’ concerns about judgment were unsubstantiated; women with unplanned pregnancies valued such discussions. Conclusions: These findings support the implementation of the LMUP in routine antenatal care and show how it can provide valuable insights into the circumstances of women’s pregnancies. This can be used to help midwives personalise care, and potentially reduce adverse outcomes and subsequent unplanned pregnancy. Integration of the LMUP into the Maternity Services Data Set, will establish national data collection for a population-level measure of unplanned pregnancy, serving as a key outcome measure for sexual and reproductive health and enabling analysis of the prevalence, factors, and implications of unplanned pregnancies across subpopulations to inform implementation. Funding : NIHR PDF-2017-10-021
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".