Longitudinal Aerodynamic Coefficients of Hydra Technologies UAS-S4 from Geometrical Data
Bibliographic record
Abstract
INTRODUCTION: Pregnancy is a crucial period which ultimately directly impacts two individuals health and wellbeing. Within the UK, a standardised pattern of care is established with collaborations across disciplines to the benefit of women and babies. During the COVID19 pandemic, this pattern of care was disrupted to align with protective protocols which until now, has not been formally reported. METHODS: A retrospective, mixed methods study of UK based women pregnant between the years 2012 and 2022 inclusive with no known complications was conducted to collate opinions and experiences of pregnancy with and without the impact of COVID19 restrictions. Quantitative results were analysed using the statistical package GraphPad Prism 9.2.0 and presented as mean values +/- standard deviation were appropriate. In addition, we used a phased approach to open ended questions. RESULTS: Our results showed no significant difference in either the number of appointments or the time of first appointment however an increased percentage of women reported the use of private services during the COVID pandemic. There was no change in the number of midwife appointments during the postnatal period during COVID but there was a significant reduction in the number of health visitor appointments. Overall, the COVID pandemic led to women feeling less satisfied with their care both during their pregnancy and postnatally, but they reported that they continued to be listened to and remained feeling in control of their pregnancy. DISCUSSION: Generally, the changes implemented during the COVID pandemic did not impact women’s pregnancy journey substantially although we have no evidence of the long-term impact on child health and development. Clear themes have been established which can be used to further improve services in maternity and there are key elements to focus on for the future of UK maternity services.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".