Risk factors for mode of delivery
Bibliographic record
Abstract
Mode of delivery and the effects of birth trauma were investigated. First, a \nliterature revealed the need for further examination of a specific mode of delivery \n(operative vaginal birth [OVB]). Secondly, an integrative review explored the \nconcept of birth trauma as it relates to healthcare provider actions and behaviours \nvia the following research question: What is known about the relationship between \nhealthcare provider actions and women’s perceived birth trauma? Thirdly, a \nretrospective study was conducted that built on the literature review to answer the \nfollowing research question: What infant, maternal, healthcare provider, and \nregional characteristics put a woman at risk for experiencing an OVB? The final \nsection concludes the project by reflecting on the work completed, interpreting the \nresults for nursing and other healthcare providers, and providing recommendations \nfor future research. This thesis sets the groundwork for future research as it \nincludes the first study to explore unique risk factors for OVB for women in \nOntario, Canada. As well as, uncovering the impact that healthcare provider \nactions and behaviours have on a woman’s birthing experience. The knowledge \nbuilt in this project has the ability to inform healthcare providers who care for \nwomen during the antenatal period, labour and childbirth as well as health policy \ninforming women’s health and wellness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".